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PopNx MCPMCP servers for people who use AI · explained in plain words
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Iris

by iris-evalversion 0.21.0i★ 10iRuns on your computer
Registry nameio.github.iris-eval/mcp-server

How to install

What it does

Stop shipping agents on vibes. Score every agent output for quality, safety, and cost.

  • Connects Runs on your computer
  • Account or key Needs IRIS_API_KEY, IRIS_WEBHOOK_SECRET
  • Category Other

Install

Pick the AI app you use, then copy the settings.

Passed 3/3 checks · Runs on your computer · ✓ Commands checked Nov 14, 2026

First: which AI do you use?

Pick your app and the steps appear below.

Works with Claude, with some setup first

You need a Iris key (create it on the maker’s site) · Only the Claude desktop app (Windows or Mac), not the web or phone

  1. Download the maker’s installer

    Download the maker’s .mcpb file

    The .mcpb file comes straight from the maker; we do not change it

  2. Double-click the file, then click Install in the Claude desktop app

  3. Try it

    Use Iris and tell me what you can do for me, then show me one thing

    ✓ If Claude says it is using Iris or shows a Iris tool in the answer, you are connected

33 optional setting(s) (works without them)

The command above works without these. Add them only if you want to change something, following the maker’s description.

  • IRIS_API_KEY_FILE Path to a file whose trimmed contents are the API key (the secret-file pattern Docker and Kubernetes mount). Set this or IRIS_API_KEY, not both; further keys and rotation are security.apiKeys in conf…
  • IRIS_ALLOW_UNAUTHENTICATED Set to 1 to run a non-loopback bind with NO API key on purpose (lifts the startup refusal; the network is then your boundary)
  • IRIS_DB_PATH SQLite database path
  • IRIS_SQLITE_DRIVER Which SQLite driver holds the database: native (better-sqlite3, the default) or node (Node's built-in node:sqlite, Node 22.13+). Unset: native, falling back to node when the native module cannot load
  • IRIS_SEARCH_BUDGET_MS How long one trace search may read before it answers with the matches found so far, in milliseconds (50-60000, default 1000)
  • IRIS_SEARCH_INDEX on (default) or off. off keeps no full-text index of the traces: writes are cheaper, and a search reads the traces within its time budget
  • IRIS_LOG_LEVEL Log level: debug, info, warn, error
  • IRIS_HOME Directory for the database, custom rules and preferences (default ~/.iris)
  • IRIS_ANTHROPIC_API_KEY Enables evaluate_with_llm_judge and verify_citations with an Anthropic model. Optional: the deterministic rules never need a key
  • IRIS_OPENAI_API_KEY Enables evaluate_with_llm_judge and verify_citations with an OpenAI model. Optional: the deterministic rules never need a key
  • IRIS_LLM_JUDGE_MAX_COST_USD_PER_EVAL Hard cap on the worst-case cost of one LLM judge call (default 0.25); max_cost_usd can lower it for a call, never raise it
  • IRIS_RELEVANCE_JUDGE_MODEL A priced judge model; answers_the_ask then asks it on every evaluation with input, sending that input and output to the model's provider, and gates on its verdict (off by default)
  • IRIS_LLM_JUDGE_DAILY_BUDGET_USD What every judge call together may spend per UTC day, per tenant, in USD (default 1): the relevance judge, evaluate_with_llm_judge and verify_citations. Past it no judge call is made
  • IRIS_RELEVANCE_JUDGE_DAILY_BUDGET_USD The name of IRIS_LLM_JUDGE_DAILY_BUDGET_USD before 0.21.0. Read when the new name is unset, and it then limits every judge call
  • IRIS_RELEVANCE_JUDGE_MAX_CALLS_PER_REQUEST Relevance judge calls one request may make (default 20)
  • IRIS_RELEVANCE_JUDGE_REDACT on (default) replaces the PII and credentials no_pii flags before the relevance judge sends input and output to its provider; off sends them as they are
  • IRIS_CITATION_ALLOW_FETCH Let verify_citations fetch URLs (off by default); a tool argument can turn it off for a call, never on
  • IRIS_DASHBOARD Start the dashboard alongside the server (same as --dashboard)
  • IRIS_DASHBOARD_PORT Dashboard port (default 6920); IRIS_PORT is the MCP HTTP transport port (default 3000)
  • IRIS_PORT MCP HTTP transport port (default 3000); only used with --transport http
  • IRIS_OTEL_ENDPOINT OTLP/HTTP collector URL; when set, every stored trace is also exported as OpenTelemetry spans (best-effort, asynchronous)
  • IRIS_CITATION_DOMAINS Comma-separated domain allowlist for verify_citations fetches (only consulted when IRIS_CITATION_ALLOW_FETCH is on); a tool argument can narrow it, never add to it
  • IRIS_CITATION_MAX_COST_USD_TOTAL The most one verify_citations call may spend on judge calls, in USD (default 1); max_cost_usd_total can lower it, never raise it
  • IRIS_TRANSPORT Transport: stdio (default) or http; same as --transport
  • IRIS_HOST Bind host for the HTTP transport (default 127.0.0.1); only used with --transport http
  • IRIS_ALLOWED_ORIGINS Comma-separated Origin allowlist for the HTTP transport and dashboard CORS (default http://localhost:*)
  • IRIS_MAX_INPUT_ELEMENTS The most array elements and object keys one input may hold before it is validated (default 100000): a tool call's arguments, and a dashboard or OTLP JSON request body. A tool call over it gets an err…
  • IRIS_MAX_TOOL_INPUT_ELEMENTS The name of IRIS_MAX_INPUT_ELEMENTS before 0.21.0, read when the new name is unset
  • IRIS_DASHBOARD_HOST Bind host for the dashboard (default 127.0.0.1); same as --dashboard-host
  • IRIS_OTEL_HEADERS Comma-separated key=value headers sent with every OTLP export (for example an auth token for your collector)
  • IRIS_OTEL_SERVICE_NAME service.name resource attribute on exported spans (default iris-eval)
  • IRIS_OTEL_TIMEOUT_MS Timeout in milliseconds for each OTLP export request
  • IRIS_WEBHOOK_URL The receiver of the webhook that fires on a moment (docs/webhooks.md); merged over notify.webhook in config.json

Descriptions are as written by the maker.

Menu names checked against Anthropic’s help page on Nov 11, 2026 · your screen may differ slightly by app version

ChatGPT cannot connect Iris yet

Iris runs on your computer, but ChatGPT can only connect servers that run on the web

What you can do instead

A · Use an app that can connect Iris

  • You need a Iris key (create it on the maker’s site) · Only the Claude desktop app (Windows or Mac), not the web or phone
  • You need a Iris key (create it on the maker’s site) · Install Node.js on your computer first (one time)
  • You need a Iris key (create it on the maker’s site) · Install Node.js on your computer first (one time)
  • You need a Iris key (create it on the maker’s site) · Install Node.js on your computer first (one time)

B · Others in Other that work with ChatGPT

Picked from the most used in the same category, only ones that connect by address with no key

Works with Claude Code, with some setup first

You need a Iris key (create it on the maker’s site) · Install Node.js on your computer first (one time)

Let AI install it

Copy this message into a Claude Code chat. The AI checks your computer, tells you which file it will change, and waits for your OK every time.

Please install the MCP server Iris for Claude Code on this computer, in this order: 1) Check Node.js with node -v. If it is missing, tell me to install it from https://nodejs.org/en/download; do not install it yourself. 2) Use only this config (already checked by PopNx MCP): claude mcp add --scope user iris-eval --env IRIS_API_KEY=your-value --env IRIS_WEBHOOK_SECRET=your-value -- npx -y @iris-eval/mcp-server 3) Tell me the full path of the file you will change, or the command you will run, and wait for my OK. 4) For IRIS_API_KEY, IRIS_WEBHOOK_SECRET, leave "your-value" in place and tell me to paste the real value myself into the file or the app’s field. Never ask me to type a key in chat. 5) Do what we agreed, then check that the Iris tools really show up.

This message has no key in it. If a key is needed, the AI will ask you to paste it yourself into the file or the app’s field.

  1. Install Node.js (if you don’t have it)

    Already installed? Check first · Open Terminal, type this command and press Enter

    node -v
    • A version number such as v26.13.1 = installed, skip this step
    • "command not found" (on Windows: "is not recognized") = not installed yet, or installed but Terminal needs to be closed and reopened
    How to open Terminal

    Mac: press ⌘ + Space, type Terminal, press Enter

    Windows: open Start, type Windows PowerShell, then Open

    Not installed: Open the official download page, pick the LTS version and the click-through installer: .pkg on Mac · .msi on Windows. Double-click and follow it to the end. No curl command needed

    Open the Node.js download page ↗

    When it’s done, close Terminal, open it again and run the same command. You should see a version number: node -v

    Install once; it works for everything that needs Node.js

  2. Sign up and create a Iris key

    Open the maker’s site, sign up and create an API key for the next step. Check the maker’s site for pricing and free plans.

    Open the Iris site ↗

  3. Open Terminal

    Mac: press Command+Space and type Terminal · Windows: open PowerShell

  4. Paste this command and press Enter

    claude mcp add --scope user iris-eval --env IRIS_API_KEY=your-value --env IRIS_WEBHOOK_SECRET=your-value -- npx -y @iris-eval/mcp-server

    Replace “your-value” with your key before pressing Enter

    Paste the key only into the app’s field, never into a chat or to anyone

  5. Type /mcp in Claude Code

    You should see Iris listed as connected

  6. Try it

    Use Iris and tell me what you can do for me, then show me one thing

    ✓ If Claude Code says it is using Iris or shows a Iris tool in the answer, you are connected

33 optional setting(s) (works without them)

The command above works without these. Add them only if you want to change something, following the maker’s description.

  • IRIS_API_KEY_FILE Path to a file whose trimmed contents are the API key (the secret-file pattern Docker and Kubernetes mount). Set this or IRIS_API_KEY, not both; further keys and rotation are security.apiKeys in conf…
  • IRIS_ALLOW_UNAUTHENTICATED Set to 1 to run a non-loopback bind with NO API key on purpose (lifts the startup refusal; the network is then your boundary)
  • IRIS_DB_PATH SQLite database path
  • IRIS_SQLITE_DRIVER Which SQLite driver holds the database: native (better-sqlite3, the default) or node (Node's built-in node:sqlite, Node 22.13+). Unset: native, falling back to node when the native module cannot load
  • IRIS_SEARCH_BUDGET_MS How long one trace search may read before it answers with the matches found so far, in milliseconds (50-60000, default 1000)
  • IRIS_SEARCH_INDEX on (default) or off. off keeps no full-text index of the traces: writes are cheaper, and a search reads the traces within its time budget
  • IRIS_LOG_LEVEL Log level: debug, info, warn, error
  • IRIS_HOME Directory for the database, custom rules and preferences (default ~/.iris)
  • IRIS_ANTHROPIC_API_KEY Enables evaluate_with_llm_judge and verify_citations with an Anthropic model. Optional: the deterministic rules never need a key
  • IRIS_OPENAI_API_KEY Enables evaluate_with_llm_judge and verify_citations with an OpenAI model. Optional: the deterministic rules never need a key
  • IRIS_LLM_JUDGE_MAX_COST_USD_PER_EVAL Hard cap on the worst-case cost of one LLM judge call (default 0.25); max_cost_usd can lower it for a call, never raise it
  • IRIS_RELEVANCE_JUDGE_MODEL A priced judge model; answers_the_ask then asks it on every evaluation with input, sending that input and output to the model's provider, and gates on its verdict (off by default)
  • IRIS_LLM_JUDGE_DAILY_BUDGET_USD What every judge call together may spend per UTC day, per tenant, in USD (default 1): the relevance judge, evaluate_with_llm_judge and verify_citations. Past it no judge call is made
  • IRIS_RELEVANCE_JUDGE_DAILY_BUDGET_USD The name of IRIS_LLM_JUDGE_DAILY_BUDGET_USD before 0.21.0. Read when the new name is unset, and it then limits every judge call
  • IRIS_RELEVANCE_JUDGE_MAX_CALLS_PER_REQUEST Relevance judge calls one request may make (default 20)
  • IRIS_RELEVANCE_JUDGE_REDACT on (default) replaces the PII and credentials no_pii flags before the relevance judge sends input and output to its provider; off sends them as they are
  • IRIS_CITATION_ALLOW_FETCH Let verify_citations fetch URLs (off by default); a tool argument can turn it off for a call, never on
  • IRIS_DASHBOARD Start the dashboard alongside the server (same as --dashboard)
  • IRIS_DASHBOARD_PORT Dashboard port (default 6920); IRIS_PORT is the MCP HTTP transport port (default 3000)
  • IRIS_PORT MCP HTTP transport port (default 3000); only used with --transport http
  • IRIS_OTEL_ENDPOINT OTLP/HTTP collector URL; when set, every stored trace is also exported as OpenTelemetry spans (best-effort, asynchronous)
  • IRIS_CITATION_DOMAINS Comma-separated domain allowlist for verify_citations fetches (only consulted when IRIS_CITATION_ALLOW_FETCH is on); a tool argument can narrow it, never add to it
  • IRIS_CITATION_MAX_COST_USD_TOTAL The most one verify_citations call may spend on judge calls, in USD (default 1); max_cost_usd_total can lower it, never raise it
  • IRIS_TRANSPORT Transport: stdio (default) or http; same as --transport
  • IRIS_HOST Bind host for the HTTP transport (default 127.0.0.1); only used with --transport http
  • IRIS_ALLOWED_ORIGINS Comma-separated Origin allowlist for the HTTP transport and dashboard CORS (default http://localhost:*)
  • IRIS_MAX_INPUT_ELEMENTS The most array elements and object keys one input may hold before it is validated (default 100000): a tool call's arguments, and a dashboard or OTLP JSON request body. A tool call over it gets an err…
  • IRIS_MAX_TOOL_INPUT_ELEMENTS The name of IRIS_MAX_INPUT_ELEMENTS before 0.21.0, read when the new name is unset
  • IRIS_DASHBOARD_HOST Bind host for the dashboard (default 127.0.0.1); same as --dashboard-host
  • IRIS_OTEL_HEADERS Comma-separated key=value headers sent with every OTLP export (for example an auth token for your collector)
  • IRIS_OTEL_SERVICE_NAME service.name resource attribute on exported spans (default iris-eval)
  • IRIS_OTEL_TIMEOUT_MS Timeout in milliseconds for each OTLP export request
  • IRIS_WEBHOOK_URL The receiver of the webhook that fires on a moment (docs/webhooks.md); merged over notify.webhook in config.json

Descriptions are as written by the maker.

Menu names checked against Anthropic’s help page on Nov 11, 2026 · your screen may differ slightly by app version

Works with Cursor, with some setup first

You need a Iris key (create it on the maker’s site) · Install Node.js on your computer first (one time)

Let AI install it

Copy this message into a Cursor chat. The AI checks your computer, tells you which file it will change, and waits for your OK every time.

Please install the MCP server Iris for Cursor on this computer, in this order: 1) Check Node.js with node -v. If it is missing, tell me to install it from https://nodejs.org/en/download; do not install it yourself. 2) Use only this config (already checked by PopNx MCP): { "mcpServers": { "iris-eval": { "command": "npx", "args": [ "-y", "@iris-eval/mcp-server" ], "env": { "IRIS_API_KEY": "your-value", "IRIS_WEBHOOK_SECRET": "your-value" } } } } 3) Tell me the full path of the file you will change, or the command you will run, and wait for my OK. 4) For IRIS_API_KEY, IRIS_WEBHOOK_SECRET, leave "your-value" in place and tell me to paste the real value myself into the file or the app’s field. Never ask me to type a key in chat. 5) Do what we agreed, then check that the Iris tools really show up.

This message has no key in it. If a key is needed, the AI will ask you to paste it yourself into the file or the app’s field.

  1. Install Node.js (if you don’t have it)

    Already installed? Check first · Open Terminal, type this command and press Enter

    node -v
    • A version number such as v26.13.1 = installed, skip this step
    • "command not found" (on Windows: "is not recognized") = not installed yet, or installed but Terminal needs to be closed and reopened
    How to open Terminal

    Mac: press ⌘ + Space, type Terminal, press Enter

    Windows: open Start, type Windows PowerShell, then Open

    Not installed: Open the official download page, pick the LTS version and the click-through installer: .pkg on Mac · .msi on Windows. Double-click and follow it to the end. No curl command needed

    Open the Node.js download page ↗

    When it’s done, close Terminal, open it again and run the same command. You should see a version number: node -v

    Install once; it works for everything that needs Node.js

  2. Sign up and create a Iris key

    Open the maker’s site, sign up and create an API key for the next step. Check the maker’s site for pricing and free plans.

    Open the Iris site ↗

  3. Click this button to open Cursor

    Open in Cursor

    Cursor asks you to confirm; click Install

  4. Fill in your key

    In the Iris settings, IRIS_API_KEY, IRIS_WEBHOOK_SECRET is empty; add your key and save

    Paste the key only into the app’s field, never into a chat or to anyone

  5. Open the AI chat in Cursor in Agent mode

  6. Try it

    Use Iris and tell me what you can do for me, then show me one thing

    ✓ If Cursor says it is using Iris or shows a Iris tool in the answer, you are connected

33 optional setting(s) (works without them)

The command above works without these. Add them only if you want to change something, following the maker’s description.

  • IRIS_API_KEY_FILE Path to a file whose trimmed contents are the API key (the secret-file pattern Docker and Kubernetes mount). Set this or IRIS_API_KEY, not both; further keys and rotation are security.apiKeys in conf…
  • IRIS_ALLOW_UNAUTHENTICATED Set to 1 to run a non-loopback bind with NO API key on purpose (lifts the startup refusal; the network is then your boundary)
  • IRIS_DB_PATH SQLite database path
  • IRIS_SQLITE_DRIVER Which SQLite driver holds the database: native (better-sqlite3, the default) or node (Node's built-in node:sqlite, Node 22.13+). Unset: native, falling back to node when the native module cannot load
  • IRIS_SEARCH_BUDGET_MS How long one trace search may read before it answers with the matches found so far, in milliseconds (50-60000, default 1000)
  • IRIS_SEARCH_INDEX on (default) or off. off keeps no full-text index of the traces: writes are cheaper, and a search reads the traces within its time budget
  • IRIS_LOG_LEVEL Log level: debug, info, warn, error
  • IRIS_HOME Directory for the database, custom rules and preferences (default ~/.iris)
  • IRIS_ANTHROPIC_API_KEY Enables evaluate_with_llm_judge and verify_citations with an Anthropic model. Optional: the deterministic rules never need a key
  • IRIS_OPENAI_API_KEY Enables evaluate_with_llm_judge and verify_citations with an OpenAI model. Optional: the deterministic rules never need a key
  • IRIS_LLM_JUDGE_MAX_COST_USD_PER_EVAL Hard cap on the worst-case cost of one LLM judge call (default 0.25); max_cost_usd can lower it for a call, never raise it
  • IRIS_RELEVANCE_JUDGE_MODEL A priced judge model; answers_the_ask then asks it on every evaluation with input, sending that input and output to the model's provider, and gates on its verdict (off by default)
  • IRIS_LLM_JUDGE_DAILY_BUDGET_USD What every judge call together may spend per UTC day, per tenant, in USD (default 1): the relevance judge, evaluate_with_llm_judge and verify_citations. Past it no judge call is made
  • IRIS_RELEVANCE_JUDGE_DAILY_BUDGET_USD The name of IRIS_LLM_JUDGE_DAILY_BUDGET_USD before 0.21.0. Read when the new name is unset, and it then limits every judge call
  • IRIS_RELEVANCE_JUDGE_MAX_CALLS_PER_REQUEST Relevance judge calls one request may make (default 20)
  • IRIS_RELEVANCE_JUDGE_REDACT on (default) replaces the PII and credentials no_pii flags before the relevance judge sends input and output to its provider; off sends them as they are
  • IRIS_CITATION_ALLOW_FETCH Let verify_citations fetch URLs (off by default); a tool argument can turn it off for a call, never on
  • IRIS_DASHBOARD Start the dashboard alongside the server (same as --dashboard)
  • IRIS_DASHBOARD_PORT Dashboard port (default 6920); IRIS_PORT is the MCP HTTP transport port (default 3000)
  • IRIS_PORT MCP HTTP transport port (default 3000); only used with --transport http
  • IRIS_OTEL_ENDPOINT OTLP/HTTP collector URL; when set, every stored trace is also exported as OpenTelemetry spans (best-effort, asynchronous)
  • IRIS_CITATION_DOMAINS Comma-separated domain allowlist for verify_citations fetches (only consulted when IRIS_CITATION_ALLOW_FETCH is on); a tool argument can narrow it, never add to it
  • IRIS_CITATION_MAX_COST_USD_TOTAL The most one verify_citations call may spend on judge calls, in USD (default 1); max_cost_usd_total can lower it, never raise it
  • IRIS_TRANSPORT Transport: stdio (default) or http; same as --transport
  • IRIS_HOST Bind host for the HTTP transport (default 127.0.0.1); only used with --transport http
  • IRIS_ALLOWED_ORIGINS Comma-separated Origin allowlist for the HTTP transport and dashboard CORS (default http://localhost:*)
  • IRIS_MAX_INPUT_ELEMENTS The most array elements and object keys one input may hold before it is validated (default 100000): a tool call's arguments, and a dashboard or OTLP JSON request body. A tool call over it gets an err…
  • IRIS_MAX_TOOL_INPUT_ELEMENTS The name of IRIS_MAX_INPUT_ELEMENTS before 0.21.0, read when the new name is unset
  • IRIS_DASHBOARD_HOST Bind host for the dashboard (default 127.0.0.1); same as --dashboard-host
  • IRIS_OTEL_HEADERS Comma-separated key=value headers sent with every OTLP export (for example an auth token for your collector)
  • IRIS_OTEL_SERVICE_NAME service.name resource attribute on exported spans (default iris-eval)
  • IRIS_OTEL_TIMEOUT_MS Timeout in milliseconds for each OTLP export request
  • IRIS_WEBHOOK_URL The receiver of the webhook that fires on a moment (docs/webhooks.md); merged over notify.webhook in config.json

Descriptions are as written by the maker.

Menu names checked against Cursor’s help page on Nov 11, 2026 · your screen may differ slightly by app version

Works with VS Code, with some setup first

You need a Iris key (create it on the maker’s site) · Install Node.js on your computer first (one time)

Let AI install it

Copy this message into a VS Code chat. The AI checks your computer, tells you which file it will change, and waits for your OK every time.

Please install the MCP server Iris for VS Code on this computer, in this order: 1) Check Node.js with node -v. If it is missing, tell me to install it from https://nodejs.org/en/download; do not install it yourself. 2) Use only this config (already checked by PopNx MCP): { "servers": { "iris-eval": { "type": "stdio", "command": "npx", "args": [ "-y", "@iris-eval/mcp-server" ], "env": { "IRIS_API_KEY": "your-value", "IRIS_WEBHOOK_SECRET": "your-value" } } } } 3) Tell me the full path of the file you will change, or the command you will run, and wait for my OK. 4) For IRIS_API_KEY, IRIS_WEBHOOK_SECRET, leave "your-value" in place and tell me to paste the real value myself into the file or the app’s field. Never ask me to type a key in chat. 5) Do what we agreed, then check that the Iris tools really show up.

This message has no key in it. If a key is needed, the AI will ask you to paste it yourself into the file or the app’s field.

  1. Install Node.js (if you don’t have it)

    Already installed? Check first · Open Terminal, type this command and press Enter

    node -v
    • A version number such as v26.13.1 = installed, skip this step
    • "command not found" (on Windows: "is not recognized") = not installed yet, or installed but Terminal needs to be closed and reopened
    How to open Terminal

    Mac: press ⌘ + Space, type Terminal, press Enter

    Windows: open Start, type Windows PowerShell, then Open

    Not installed: Open the official download page, pick the LTS version and the click-through installer: .pkg on Mac · .msi on Windows. Double-click and follow it to the end. No curl command needed

    Open the Node.js download page ↗

    When it’s done, close Terminal, open it again and run the same command. You should see a version number: node -v

    Install once; it works for everything that needs Node.js

  2. Sign up and create a Iris key

    Open the maker’s site, sign up and create an API key for the next step. Check the maker’s site for pricing and free plans.

    Open the Iris site ↗

  3. Click this button to open VS Code

    Open in VS Code

    VS Code asks you to confirm; click Install

  4. Fill in your key

    In the Iris settings, IRIS_API_KEY, IRIS_WEBHOOK_SECRET is empty; add your key and save

    Paste the key only into the app’s field, never into a chat or to anyone

  5. Open the AI chat in VS Code in Agent mode

  6. Try it

    Use Iris and tell me what you can do for me, then show me one thing

    ✓ If VS Code says it is using Iris or shows a Iris tool in the answer, you are connected

33 optional setting(s) (works without them)

The command above works without these. Add them only if you want to change something, following the maker’s description.

  • IRIS_API_KEY_FILE Path to a file whose trimmed contents are the API key (the secret-file pattern Docker and Kubernetes mount). Set this or IRIS_API_KEY, not both; further keys and rotation are security.apiKeys in conf…
  • IRIS_ALLOW_UNAUTHENTICATED Set to 1 to run a non-loopback bind with NO API key on purpose (lifts the startup refusal; the network is then your boundary)
  • IRIS_DB_PATH SQLite database path
  • IRIS_SQLITE_DRIVER Which SQLite driver holds the database: native (better-sqlite3, the default) or node (Node's built-in node:sqlite, Node 22.13+). Unset: native, falling back to node when the native module cannot load
  • IRIS_SEARCH_BUDGET_MS How long one trace search may read before it answers with the matches found so far, in milliseconds (50-60000, default 1000)
  • IRIS_SEARCH_INDEX on (default) or off. off keeps no full-text index of the traces: writes are cheaper, and a search reads the traces within its time budget
  • IRIS_LOG_LEVEL Log level: debug, info, warn, error
  • IRIS_HOME Directory for the database, custom rules and preferences (default ~/.iris)
  • IRIS_ANTHROPIC_API_KEY Enables evaluate_with_llm_judge and verify_citations with an Anthropic model. Optional: the deterministic rules never need a key
  • IRIS_OPENAI_API_KEY Enables evaluate_with_llm_judge and verify_citations with an OpenAI model. Optional: the deterministic rules never need a key
  • IRIS_LLM_JUDGE_MAX_COST_USD_PER_EVAL Hard cap on the worst-case cost of one LLM judge call (default 0.25); max_cost_usd can lower it for a call, never raise it
  • IRIS_RELEVANCE_JUDGE_MODEL A priced judge model; answers_the_ask then asks it on every evaluation with input, sending that input and output to the model's provider, and gates on its verdict (off by default)
  • IRIS_LLM_JUDGE_DAILY_BUDGET_USD What every judge call together may spend per UTC day, per tenant, in USD (default 1): the relevance judge, evaluate_with_llm_judge and verify_citations. Past it no judge call is made
  • IRIS_RELEVANCE_JUDGE_DAILY_BUDGET_USD The name of IRIS_LLM_JUDGE_DAILY_BUDGET_USD before 0.21.0. Read when the new name is unset, and it then limits every judge call
  • IRIS_RELEVANCE_JUDGE_MAX_CALLS_PER_REQUEST Relevance judge calls one request may make (default 20)
  • IRIS_RELEVANCE_JUDGE_REDACT on (default) replaces the PII and credentials no_pii flags before the relevance judge sends input and output to its provider; off sends them as they are
  • IRIS_CITATION_ALLOW_FETCH Let verify_citations fetch URLs (off by default); a tool argument can turn it off for a call, never on
  • IRIS_DASHBOARD Start the dashboard alongside the server (same as --dashboard)
  • IRIS_DASHBOARD_PORT Dashboard port (default 6920); IRIS_PORT is the MCP HTTP transport port (default 3000)
  • IRIS_PORT MCP HTTP transport port (default 3000); only used with --transport http
  • IRIS_OTEL_ENDPOINT OTLP/HTTP collector URL; when set, every stored trace is also exported as OpenTelemetry spans (best-effort, asynchronous)
  • IRIS_CITATION_DOMAINS Comma-separated domain allowlist for verify_citations fetches (only consulted when IRIS_CITATION_ALLOW_FETCH is on); a tool argument can narrow it, never add to it
  • IRIS_CITATION_MAX_COST_USD_TOTAL The most one verify_citations call may spend on judge calls, in USD (default 1); max_cost_usd_total can lower it, never raise it
  • IRIS_TRANSPORT Transport: stdio (default) or http; same as --transport
  • IRIS_HOST Bind host for the HTTP transport (default 127.0.0.1); only used with --transport http
  • IRIS_ALLOWED_ORIGINS Comma-separated Origin allowlist for the HTTP transport and dashboard CORS (default http://localhost:*)
  • IRIS_MAX_INPUT_ELEMENTS The most array elements and object keys one input may hold before it is validated (default 100000): a tool call's arguments, and a dashboard or OTLP JSON request body. A tool call over it gets an err…
  • IRIS_MAX_TOOL_INPUT_ELEMENTS The name of IRIS_MAX_INPUT_ELEMENTS before 0.21.0, read when the new name is unset
  • IRIS_DASHBOARD_HOST Bind host for the dashboard (default 127.0.0.1); same as --dashboard-host
  • IRIS_OTEL_HEADERS Comma-separated key=value headers sent with every OTLP export (for example an auth token for your collector)
  • IRIS_OTEL_SERVICE_NAME service.name resource attribute on exported spans (default iris-eval)
  • IRIS_OTEL_TIMEOUT_MS Timeout in milliseconds for each OTLP export request
  • IRIS_WEBHOOK_URL The receiver of the webhook that fires on a moment (docs/webhooks.md); merged over notify.webhook in config.json

Descriptions are as written by the maker.

Menu names checked against VS Code’s help page on Nov 11, 2026 · your screen may differ slightly by app version

Other apps that use MCP, such as Windsurf, Zed or Gemini CLI, each add servers differently. See how in the app list.

See every app that uses MCP →

JSON settings are under “For developers” below

Stuck? Ask people who use Iris💬 Discussion →
For developers: Terminal commands and JSON settings

Installed on your computer

Claude Code

claude mcp add --scope user iris-eval --env IRIS_API_KEY=your-value --env IRIS_WEBHOOK_SECRET=your-value -- npx -y @iris-eval/mcp-server

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "iris-eval": {
      "command": "npx",
      "args": [
        "-y",
        "@iris-eval/mcp-server"
      ],
      "env": {
        "IRIS_API_KEY": "your-value",
        "IRIS_WEBHOOK_SECRET": "your-value"
      }
    }
  }
}

Cursor (~/.cursor/mcp.json)

{
  "mcpServers": {
    "iris-eval": {
      "command": "npx",
      "args": [
        "-y",
        "@iris-eval/mcp-server"
      ],
      "env": {
        "IRIS_API_KEY": "your-value",
        "IRIS_WEBHOOK_SECRET": "your-value"
      }
    }
  }
}

VS Code (mcp.json)

{
  "servers": {
    "iris-eval": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@iris-eval/mcp-server"
      ],
      "env": {
        "IRIS_API_KEY": "your-value",
        "IRIS_WEBHOOK_SECRET": "your-value"
      }
    }
  }
}

Values you fill in

  • IRIS_API_KEY secret required env
    API key for HTTP authentication. Required to bind the HTTP transport or the dashboard beyond loopback (0.0.0.0, a LAN address, a container): without it the server refuses to start
  • IRIS_API_KEY_FILE env
    Path to a file whose trimmed contents are the API key (the secret-file pattern Docker and Kubernetes mount). Set this or IRIS_API_KEY, not both; further keys and rotation are security.apiKeys in conf…
  • IRIS_ALLOW_UNAUTHENTICATED env
    Set to 1 to run a non-loopback bind with NO API key on purpose (lifts the startup refusal; the network is then your boundary)
  • IRIS_DB_PATH env
    SQLite database path
  • IRIS_SQLITE_DRIVER env
    Which SQLite driver holds the database: native (better-sqlite3, the default) or node (Node's built-in node:sqlite, Node 22.13+). Unset: native, falling back to node when the native module cannot load
  • IRIS_SEARCH_BUDGET_MS env
    How long one trace search may read before it answers with the matches found so far, in milliseconds (50-60000, default 1000)
  • IRIS_SEARCH_INDEX env
    on (default) or off. off keeps no full-text index of the traces: writes are cheaper, and a search reads the traces within its time budget
  • IRIS_LOG_LEVEL env
    Log level: debug, info, warn, error
  • IRIS_HOME env
    Directory for the database, custom rules and preferences (default ~/.iris)
  • IRIS_ANTHROPIC_API_KEY secret env
    Enables evaluate_with_llm_judge and verify_citations with an Anthropic model. Optional: the deterministic rules never need a key
  • IRIS_OPENAI_API_KEY secret env
    Enables evaluate_with_llm_judge and verify_citations with an OpenAI model. Optional: the deterministic rules never need a key
  • IRIS_LLM_JUDGE_MAX_COST_USD_PER_EVAL env
    Hard cap on the worst-case cost of one LLM judge call (default 0.25); max_cost_usd can lower it for a call, never raise it
  • IRIS_RELEVANCE_JUDGE_MODEL env
    A priced judge model; answers_the_ask then asks it on every evaluation with input, sending that input and output to the model's provider, and gates on its verdict (off by default)
  • IRIS_LLM_JUDGE_DAILY_BUDGET_USD env
    What every judge call together may spend per UTC day, per tenant, in USD (default 1): the relevance judge, evaluate_with_llm_judge and verify_citations. Past it no judge call is made
  • IRIS_RELEVANCE_JUDGE_DAILY_BUDGET_USD env
    The name of IRIS_LLM_JUDGE_DAILY_BUDGET_USD before 0.21.0. Read when the new name is unset, and it then limits every judge call
  • IRIS_RELEVANCE_JUDGE_MAX_CALLS_PER_REQUEST env
    Relevance judge calls one request may make (default 20)
  • IRIS_RELEVANCE_JUDGE_REDACT env
    on (default) replaces the PII and credentials no_pii flags before the relevance judge sends input and output to its provider; off sends them as they are
  • IRIS_CITATION_ALLOW_FETCH env
    Let verify_citations fetch URLs (off by default); a tool argument can turn it off for a call, never on
  • IRIS_DASHBOARD env
    Start the dashboard alongside the server (same as --dashboard)
  • IRIS_DASHBOARD_PORT env
    Dashboard port (default 6920); IRIS_PORT is the MCP HTTP transport port (default 3000)
  • IRIS_PORT env
    MCP HTTP transport port (default 3000); only used with --transport http
  • IRIS_OTEL_ENDPOINT env
    OTLP/HTTP collector URL; when set, every stored trace is also exported as OpenTelemetry spans (best-effort, asynchronous)
  • IRIS_CITATION_DOMAINS env
    Comma-separated domain allowlist for verify_citations fetches (only consulted when IRIS_CITATION_ALLOW_FETCH is on); a tool argument can narrow it, never add to it
  • IRIS_CITATION_MAX_COST_USD_TOTAL env
    The most one verify_citations call may spend on judge calls, in USD (default 1); max_cost_usd_total can lower it, never raise it
  • IRIS_TRANSPORT env
    Transport: stdio (default) or http; same as --transport
  • IRIS_HOST env
    Bind host for the HTTP transport (default 127.0.0.1); only used with --transport http
  • IRIS_ALLOWED_ORIGINS env
    Comma-separated Origin allowlist for the HTTP transport and dashboard CORS (default http://localhost:*)
  • IRIS_MAX_INPUT_ELEMENTS env
    The most array elements and object keys one input may hold before it is validated (default 100000): a tool call's arguments, and a dashboard or OTLP JSON request body. A tool call over it gets an err…
  • IRIS_MAX_TOOL_INPUT_ELEMENTS env
    The name of IRIS_MAX_INPUT_ELEMENTS before 0.21.0, read when the new name is unset
  • IRIS_DASHBOARD_HOST env
    Bind host for the dashboard (default 127.0.0.1); same as --dashboard-host
  • IRIS_OTEL_HEADERS env
    Comma-separated key=value headers sent with every OTLP export (for example an auth token for your collector)
  • IRIS_OTEL_SERVICE_NAME env
    service.name resource attribute on exported spans (default iris-eval)
  • IRIS_OTEL_TIMEOUT_MS env
    Timeout in milliseconds for each OTLP export request
  • IRIS_WEBHOOK_URL env
    The receiver of the webhook that fires on a moment (docs/webhooks.md); merged over notify.webhook in config.json
  • IRIS_WEBHOOK_SECRET secret required env
    The signing key of that webhook (any string, or whsec_ + base64); the iris format refuses to run without one

Descriptions are as written by the maker.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

At a glance

Downloads per weeki
134/wk
Connection
Runs on your computer
Latest versioni
0.21.0
Categoryi
Other
Published to registryi
Oct 26, 2026
Code last changedi
Nov 14, 2026
Maker
iris-eval
Packages
npm: @iris-eval/mcp-server
mcpb: …/iris-eval.mcpb
Registry status
Active

Which apps can use it

Based on how this server connects and on each app’s own docs, checked Nov 10, 2026.

Run on your computer in

ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast

All apps, with pros and cons →

Related guides

Guide 85 checks before you install an MCPAbout 2 minutes each: check its page on our site and the permission request in your AI app before you say yes.4 min read

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 16, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account iris-eval, which owns the repo

  • ✓ Pass

    Still maintained

    Code last changed 2 days ago

  • ✓ Pass

    Has a licence

    Uses MIT

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Published vulnerabilities (OSV.dev)

No entries on OSV.dev for @iris-eval/mcp-server@0.21.0

Checks only this package version, not every dependency · checked Nov 15, 2026

What we check → · JSON

Makers: ask for a re-check or report wrong info

Sign in

Discussion

Ask, share a tip or report that it does not work. Sorted by what people marked helpful.

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Posts here are written by users, not the PopNx team.

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User reviews

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Compared with others in Otheri

  • More stars than 96% of the other 19960 servers in this category
  • Published to the registry more recently than 67% of the other 19960 servers in this category
  • Basic checks equal to or better than 100% of the other 19960 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Iris safe?

It passed 3 of 3 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: IRIS_API_KEY, IRIS_WEBHOOK_SECRET

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed 2 days ago, and the latest version (0.21.0) reached the registry 21 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js, MCPB on your computer.

What can I use instead?

In the same category: World Monitor, openstatus, Oh My Posh Validator, Strata, Jitsu

Similar alternatives

Same category, other makers, commands already checked. Official ones first, then by stars.

Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

World Monitor

Real-time global intelligence on conflict, markets, energy, trade, shipping and cyber. 97 tools.

by koala73Widely used★ 90.7KUpdated 2 days ago

Runs in the cloud✓ service owner · worldmonitor.app
How to connect

openstatus

Manage monitors, status pages, incidents and maintenances in your openstatus workspace.

by openstatusHQWidely used★ 9.3KUpdated 2 months ago

Runs in the cloud✓ service owner · openstatus.dev
How to connect

Oh My Posh Validator

Validate oh-my-posh configurations and segment snippets against the official schema.

by JanDeDobbeleerWidely used★ 23.8K Stars for the whole repoUpdated 27 days ago

Runs in the cloud✓ service owner · ohmyposh.dev
How to connect

Strata

MCP server for progressive tool usage at any scale (see https://klavis.ai)

by Klavis-AIGrowing★ 5.8KUpdated 14 months ago

Runs in the cloudRepo archived✓ klavis.ai
How to connect

Jitsu

Manage Jitsu data pipelines: destinations, streams, connections, functions, live events.

by jitsucomWidely used★ 5.1KUpdated 4 months ago

Runs in the cloud✓ service owner · jitsu.com
How to connect
  • World Monitor — Runs in the cloud, nothing to install · No key needed
  • openstatus — Runs in the cloud, nothing to install · No key needed
  • Oh My Posh Validator — Runs in the cloud, nothing to install · No key needed
  • Strata — Runs in the cloud, nothing to install · No key needed
  • Jitsu — Runs in the cloud, nothing to install · No key needed

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Iris — stop shipping agents on vibes

Send Iris an agent run and ask for a verdict on its quality, safety, and cost — on your machine, with no SDK and no account. Most agent projects check quality by running a few remembered prompts and eyeballing the output. Iris replaces that with numbers you can audit: your agent's runs land in a SQLite database on your disk, 26 built-in rules score them deterministically — PII, prompt injection, hallucination markers, cost thresholds, and the agent's own tool calls — free, with no LLM calls, and an optional LLM judge with a hard per-eval cost cap handles the semantic questions. Every rule is inspectable and editable, because a judge you can't audit is just vibes with a number on it. MIT licensed, no telemetry. Nothing leaves your machine unless you turn one of these on: an OpenTelemetry endpoint (IRIS_OTEL_ENDPOINT), which exports traces to the collector you name; the LLM judge with your own key, which sends the text it judges to that provider, and whose citation check fetches the pages an output cites; or a webhook, which posts ids, the verdict and rule names, never the text, to the address you set.

Requires Node.js 22.13 or later. Check with node --version.

The demo database, recorded by scripts/demo-media.mts; the source is demo.mp4. A still: dashboard-overview.png.

A failure on screen in 60 seconds

No agent wiring, no config — one command:

npx @iris-eval/mcp-server --demo

This seeds a demo database — five small agents, two weeks of runs, every verdict the engine's own — and serves the dashboard against it at http://localhost:6920 (your browser opens automatically on first run). The dashboard lands on Failures: what failed, worst and newest first, each card naming the rule and its evidence. Worth clicking into — a PII leak caught by the safety rules, a hidden directive in a forum post that the summarizer complied with, a number the source document never said, two runs on the same twelve questions compared with an interval (Runs), a deployed custom rule and a paused one with their audit rows, and a failed LLM-judge score with its rationale.

Demo data lives in its own database (demo.db in your Iris home directory — ~/.iris on macOS/Linux, %USERPROFILE%\.iris on Windows) and never mixes with your real traces. Remove all of it with one command:

npx @iris-eval/mcp-server --demo-clear

Hook up your own agent

First, prove the install works on this machine — it runs offline and opens nothing of yours:

npx @iris-eval/mcp-server --self-test   # exit 0 = healthy

Then add Iris to your MCP client. One command writes the client's own config file, keeps every other server in it, and pins the version you ran:

npx -y @iris-eval/mcp-server install claude-code

The clients: claude-code, claude-desktop, cursor, windsurf, continue, vscode, cline, zed, codex, gemini. install --list shows the ones found on this machine, the Iris each one runs and the file it reads; install --uninstall takes Iris out again. Every client shares one database, so after an upgrade move them all at once with install --upgrade (Updating). Restart the client to load it.

Claude Desktop: one click. Each release from 0.20.0 on attaches iris-eval.mcpb, an MCP Bundle: download the latest, open it, and Claude Desktop shows an install dialog. Nothing on it is required — an Anthropic or OpenAI key for the LLM judge is optional, and the dashboard is a switch that starts off. The bundle holds the npm package and its dependencies, so nothing else needs installing: Claude Desktop runs it under the Node it ships when that Node is 22.13 or newer (Claude Desktop 1.1.6679 ships 24.13), and Iris stores traces with Node's built-in SQLite, in the same ~/.iris every other install uses. The release notes show how to verify its signature and build attestation.

It runs in any MCP client, and every client it names has a row saying how it is checked. Verified on every CI run: Claude Code, Gemini CLI — the real client starts Iris from the config the installer wrote and reports it connected (claude mcp list, gemini mcp list), on Linux, macOS and Windows; Claude Code's capture plugin hooks are driven through the real scripts too. Configured from each client's own MCP documentation — the installer writes the file each client documents, at its documented path and in its own format (an mcpServers block, VS Code's servers, Zed's context_servers, Codex's TOML), keeping comments and every other server, and unit tests hold each path and every format writer to that documentation: Claude Desktop, Cursor, Devin Desktop (Windsurf), Continue, VS Code, Cline, Zed, OpenAI Codex CLI. Every row with its source and the date it was read: https://iris-eval.com/clients. By hand instead, one block, dashboard included:

{

"mcpServers": { "iris-eval": { "command": "npx", "args": ["-y", "@iris-eval/mcp-server", "--dashboard"] } } }

Your client lists Iris's twelve tools on connect, and the dashboard serves at http://localhost:6920. Now paste this to your agent:

Log that last task to Iris and evaluate the output.

The trace lands on the dashboard with its scores. Prefer the MCP server headless? Drop --dashboard from the args — you can open the same dashboard any time with npx @iris-eval/mcp-server --dashboard.

One thing worth knowing up front: MCP tools are called when the model decides to call them. Iris doesn't intercept your agent, so traces are logged when your agent asks it to log them — either because you told it to, or because your code calls the tools directly. Ask your agent to "log this to Iris and evaluate it" and it will. If you want capture that doesn't depend on the model choosing, POST /api/v1/traces does exactly that — your code sends the trace over plain HTTP, no model in the loop (see docs/http-ingest.md). The CLI and host hooks on the roadmap will be thin clients over the same endpoint.

Capture over HTTP (no model in the loop)

The ingest endpoint lives on the dashboard port — 6920 by default, not the MCP transport port — and it exists only while the dashboard is running. Pass --dashboard (or set IRIS_DASHBOARD=true); --transport http on its own does not start it, and a request to the transport port returns 404. With the dashboard up, anything that can send an HTTP request can log a trace — and optionally run the deterministic evals in the same request. GET /api/v1/capabilities on the same port says what this server can judge, what each rule needs, the judge state with the steps that enable it, and the limits — the same object the MCP resource iris://capabilities serves — so an HTTP caller has the frame an MCP client gets at initialize:

curl -s -X POST "http://127.0.0.1:6920/api/v1/traces" \

-H "Content-Type: application/json" \ -d '{ "agent_name": "support-bot", "input": "What is the refund policy?", "output": "Refunds are available within 30 days of purchase.", "evaluate": true, "eval_type": "safety" }'

Returns 201 with the stored trace_id and the evaluation result (in --demo mode the endpoint refuses writes with 403, so demo data never mixes with yours). The endpoint accepts the same body as the log_trace tool and sits behind the same middleware stack as the rest of the dashboard: loopback bind and the DNS-rebinding guard by default, plus Bearer auth when you set one. Two plain facts about it: it accepts unauthenticated writes unless Iris was started with --api-key (or IRIS_API_KEY) — the loopback bind is what keeps it to your machine by default, so set a key before binding beyond loopback; and what it stores is verbatim — input and output land in iris.db exactly as sent, including any text no_pii goes on to flag. Full contract, field reference, and error semantics: docs/http-ingest.md.

Capture every Claude Code turn (optional)
/plugin marketplace add iris-eval/mcp-server

/plugin install iris-eval-capture@iris-eval

A second, separately installed plugin: its hooks record each turn's prompt, tool calls (the failed ones included, with the error the agent received) and final answer, keyed on the prompt, so an interrupted turn's calls and a background sub-agent's are sent too, and hand them to iris-eval ingest, detached, with critical spans redacted in the stored evaluation text — capture that does not depend on the model deciding to call a tool. A turn that ended in an API error is stored without being judged; when the model logged the turn too, both traces are kept and the plugin's names the model's. It never prints, never blocks, never sends anything anywhere. Installing iris-eval alone changes nothing about your turn loop. Limits and removal: claude-plugin-capture/README.md.

Python
pip install iris-eval
from iris_eval import IrisClient

iris = IrisClient() # IRIS_URL, or the running dashboard's runtime.json iris.evaluate_output("…", input="…", agent_name="support-bot")["verdict"] # {"state": "pass", "basis": "clean", "by": []}

A thin client over the HTTP API of server 0.16.0 and later, versioned on its own — iris_eval.__version__ and the PyPI page carry its number, which is not the server's: log_trace(), evaluate_output(), get_traces(), get_trace(), health(), capabilities(), sync and async, typed answers, the server's own sentence on a refusal — and a pytest plugin: an iris fixture and assert_iris(output, expect="pass") that asserts on the verdict's state. packages/python/README.md.

Record every OpenAI and Anthropic call
from iris_eval import wrap_openai

client = wrap_openai(OpenAI(), agent_name="support-bot") # every call: a GenAI span to POST /v1/traces, scored

import { wrapOpenAI } from '@iris-eval/sdk';

c

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