Prototype · Preview: every server in the official registry, synced every 30 minutes. Sign in with Google to write reviews.Prototype · updated every 30 min
PopNx MCPMCP servers for people who use AI · explained in plain words

Evr

by evidence-readinessversion 0.1.2i★ 0iRuns on your computer
Registry nameio.github.BrienBounded/evr

How to install

What it does

Checks agent traces for evidence-readiness: verifies evidence is present, not that it is true.

  • Connects Runs on your computer
  • Account or key No key needed
  • Category Other

Install

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

Passed 2/3 checks · Runs on your computer · ✓ Commands checked Nov 13, 2026

First: which AI do you use?

Pick your app and the steps appear below.

Works with Claude, with some setup first

Install Python (uv) on your computer first (one time) · Only the Claude desktop app (Windows or Mac), not the web or phone

  1. Install Python (uv)

    One time only; it works for every server that needs Python (uv)

    Open the Python (uv) download page ↗

  2. Open the Claude desktop app’s settings file

    SettingsDeveloperEdit Config

  3. Paste these settings and save the file

    {
      "mcpServers": {
        "evr": {
          "command": "uvx",
          "args": [
            "evidence-readiness"
          ]
        }
      }
    }
  4. Quit Claude and open it again

  5. Try it

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

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

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

ChatGPT cannot connect Evr yet

Evr 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 Evr

  • Install Python (uv) on your computer first (one time) · Only the Claude desktop app (Windows or Mac), not the web or phone
  • Install Python (uv) on your computer first (one time)
  • Install Python (uv) on your computer first (one time)
  • Install Python (uv) 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

Install Python (uv) on your computer first (one time)

  1. Install Python (uv)

    One time only; it works for every server that needs Python (uv)

    Open the Python (uv) download page ↗

  2. Open Terminal

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

  3. Paste this command and press Enter

    claude mcp add --scope user evr -- uvx evidence-readiness
  4. Type /mcp in Claude Code

    You should see Evr listed as connected

  5. Try it

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

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

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

Install Python (uv) on your computer first (one time)

  1. Install Python (uv)

    One time only; it works for every server that needs Python (uv)

    Open the Python (uv) download page ↗

  2. Click this button to open Cursor

    Open in Cursor

    Cursor asks you to confirm; click Install

  3. Open the AI chat in Cursor in Agent mode

  4. Try it

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

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

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

Install Python (uv) on your computer first (one time)

  1. Install Python (uv)

    One time only; it works for every server that needs Python (uv)

    Open the Python (uv) download page ↗

  2. Click this button to open VS Code

    Open in VS Code

    VS Code asks you to confirm; click Install

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

  4. Try it

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

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

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 Evr💬 Discussion →
For developers: Terminal commands and JSON settings

Installed on your computer

Claude Code

claude mcp add --scope user evr -- uvx evidence-readiness

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "evr": {
      "command": "uvx",
      "args": [
        "evidence-readiness"
      ]
    }
  }
}

Cursor (~/.cursor/mcp.json)

{
  "mcpServers": {
    "evr": {
      "command": "uvx",
      "args": [
        "evidence-readiness"
      ]
    }
  }
}

VS Code (mcp.json)

{
  "servers": {
    "evr": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "evidence-readiness"
      ]
    }
  }
}

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

At a glance

Downloads per weeki
18/wk
Licencei
Apache-2.0
Connection
Runs on your computer
Latest versioni
0.1.2
Categoryi
Other
Published to registryi
Aug 12, 2026
Code last changedi
Aug 12, 2026
Maker
evidence-readiness
Packages
pypi: evidence-readiness
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

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 14, 2026

  • ! Caution

    Maker matches the repo

    The publisher (BrienBounded) is not the repo owner (evidence-readiness); someone else may have listed it

  • ✓ Pass

    Still maintained

    Code last changed 3 months ago

  • ✓ Pass

    Has a licence

    Uses Apache-2.0

  • – 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

Discussion

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

No posts yet. Be the first to ask or share.

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

Sign in with Google to post

User reviews

No reviews yet

Sign in with Google to write a review

Compared with others in Otheri

  • More stars than 0% of the other 19729 servers in this category
  • Published to the registry more recently than 18% of the other 19729 servers in this category
  • Basic checks equal to or better than 45% of the other 19729 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 Evr safe?

It passed 2 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?

The registry does not list a required key, though some servers ask you to sign in on first use.

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 3 months ago, and the latest version (0.1.2) reached the registry 3 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: World Monitor, Claude Flow, Windmill

Others in the same category

Servers in the same category from other makers, sorted 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★ 90.6KUpdated yesterday

Runs in the cloud✓ service owner · worldmonitor.app

Claude Flow

AI orchestration with hive-mind swarms, neural networks, and 87 MCP tools for enterprise dev.

by ruvnetWidely used↓ 13.5K/wk★ 81.6KUpdated 14 months ago

Runs on your computer

Windmill

Run Windmill scripts and flows and manage jobs, schedules, resources and variables.

by windmill-labsWidely used★ 18.6KUpdated 2 days ago

Runs in the cloud

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.

evidence-readiness

Checks whether an agent execution trace contains the evidence a post-hoc causal reconstruction would require.

When an AI agent causes a loss — a bad config change, a cross-session data leak, contaminated output cascading between agents, a prompt injection — someone eventually has to reconstruct *why* from the trace. Traces from general-purpose agent instrumentation cannot support that reconstruction: the evidence was never recorded. This repository contains the evidence-readiness specification (SPEC.md), a checker that evaluates a trace against it, and reference fixtures showing what conformant instrumentation looks like.

What this is — and is not

  • **It checks that evidence is *present*, not that it is *true*.** A fabricated-but-internally-consistent trace passes. This tool authenticates nothing, and its result must never be relayed as "this agent is safe."
  • It never determines whether a failure occurred. It answers "does this trace contain what a reconstruction of class X would require," never "did class X occur." It contains no detection or scoring logic.
  • This repository does not contain the reconstruction engine. The specification is published so the method is assessable; the engine is available for inspection under the terms in SOURCE_AVAILABILITY.md.
  • Four failure classes are covered: config_drift, session_isolation_failure, cascade_contamination, prompt_injection. A fifth (tool_misuse) was investigated and declined for stated structural reasons — a documented negative result, not a gap (SPEC.md §8).
  • The spec is a profile of the OTel GenAI semantic conventions: core OTel plus documented extensions (SPEC.md §2). It carries no standards-body endorsement.

Who it is for

  • Engineers instrumenting agents who want incidents to be reconstructable after the fact — diff your trace against a conformant fixture and see what to add.
  • Reviewers (insurers, auditors, counterparties) evaluating whether an agent system's telemetry would support retrospective loss adjudication.
  • Coding agents: see llms.txt, spec.json (the machine-readable spec export, generated — never hand-maintained), and the MCP server in src/evidence_readiness/mcp_server.py (configuration below).

Quickstart (60 seconds)

# from a clone of this repository

pip install . evr check path/to/trace.json # or a directory of traces evr check --json path/to/traces/ # machine-readable report evr check --require-class prompt_injection path/to/trace.json

The package has zero runtime dependencies; pip install . in any Python ≥3.10 environment is the whole setup. Equivalent uvx forms, both exercised: uvx --from . evr check ./traces from a clone, and uvx --from evidence-readiness evr check ./traces from the package index.

Both need `--from`, and the trailing `evr` is not optional. A bare uvx evidence-readiness ... runs the console script *named for the distribution*, and that script is the MCP server (below), not the checker — it reads standard input, ignores the arguments, and exits successfully without checking anything.

Exit codes carry no reconstruction vocabulary: 0 all requirements met · 1 one or more requirements partially met · 2 one or more requirements unmet · 3 malformed input (SPEC.md §9 is the source of record). Universal requirements (EVR-R1..R6) drive the exit code; per-class evidence readiness is reported separately, and --require-class folds a named class's readiness into the exit code. For every failed requirement the output names the specific missing field and the span kind it belongs on.

Every run ends with the same statement this README opened with: presence, not truth — evidence being present does not make it authentic, and a passing trace is not a safe agent.

CI: check every PR (GitHub Action)

A spec is read once; a check that runs on every PR changes instrumentation behavior. Add this to a workflow in the repository that produces your traces (evidence-readiness is this repository's owner once published; from within this repository itself, uses: ./.github/actions/readiness-check):

steps:
  • uses: actions/checkout@v4
  • uses: evidence-readiness/spec/.github/actions/readiness-check@main with: trace-path: traces/

That is the whole setup — the default GitHub runner's python3 is sufficient, and the Action installs the (zero-dependency) checker itself. The step fails when the checker's exit code exceeds minimum-passing-exit-level (default 0; exit-code semantics as in the Quickstart above). Optional required-classes folds named classes' evidence requirements into the exit code. Outputs: exit-code, exit-meaning, report-path (full JSON report), and per-class-readiness. PR annotations name each incomplete requirement's specific missing field and span kind. Every annotation is a statement about the trace telemetry, never about the agent: an annotated PR has incomplete evidence, not a detected failure — and a clean run is not a safe agent. This repository's own CI (.github/workflows/ci.yml) dogfoods the Action against fixtures/, asserting the expected — deliberately nonzero — exit codes per bucket.

MCP server (agent surface)

src/evidence_readiness/mcp_server.py exposes the checker and the requirement data to agents over the Model Context Protocol (stdio). Three tools: check_trace_readiness (file, directory, or inline JSON — inline content is processed in memory and never written anywhere), get_requirement, and list_class_requirements.

Installing the package delivers three console scripts, and it is worth being precise about which is which before you copy anything below:

scriptwhat it runs
evrthe checker CLI — this is the one a human wants
evr-mcpthe MCP server, on stdio
evidence-readinessthe MCP server, on stdio — same target as evr-mcp, named for the distribution so that per-invocation runners which resolve to a like-named script find it

So evidence-readiness is not the checker. Typing it at a shell gets a server waiting for JSON-RPC on stdin, which looks like a hang. Use evr.

If the package is installed, the evr-mcp console script is the whole configuration:

{

"mcpServers": { "evidence-readiness": { "command": "evr-mcp" } } }

From a clone, with nothing installed, run the module by path instead:

{

"mcpServers": { "evidence-readiness": { "command": "python3", "args": [ "/absolute/path/to/evidence-readiness/src/evidence_readiness/mcp_server.py" ] } } }

Either way it needs no dependencies, and the second form needs no installation. It is local-only: no network calls, no phone-home, nothing persisted. It deliberately has no submission tool — the server never transmits anything; submitting a trace anywhere is a human decision gated on the CONTRIBUTING.md disclaimer and a redaction judgment an agent cannot make. Every tool description carries the presence-not-truth statement, so a readiness result cannot reasonably be relayed as "this agent is safe."

Status

The specification is versioned (SPEC.md carries its current version and correction log) and the checker is implemented against it, with reference fixtures (fixtures/), the machine-readable export (spec.json), the agent surface (llms.txt, the MCP server), and the CI action (.github/actions/readiness-check/) in place. The methodology and limitations paper is drafted (PAPER.md); its front-matter banner records which sections have had an adversarial read and which have not. The standing disclosure policy is SOURCE_AVAILABILITY.md; reporting and trace-submission terms are CONTRIBUTING.md, with redaction guidance in REDACTION.md and release verification in RELEASING.md. Corrections are logged in CHANGELOG.md. The spec is descriptive: every requirement is backed by an observed outcome in a validation record of 240 reconstruction attempts, 222 of them synthetic — backing is per-requirement, from the specific cases that exercised it, not a property measured across all 240 (18 real-world Who&When cases validate the abstention direction only; the split is stated wherever the count appears — SPEC.md §1).

Attribution

Brien Buckman

Licensed under Apache-2.0 (see LICENSE).

The substance of this repository's development record is public: corrections that affect published content are logged in CHANGELOG.md, and the development defect record, as it bears on the method, is summarized in PAPER.md §8.6. The commit history in which these files were drafted and revised is not published.