Context7
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Description from the maker (original English)
Prompt evals over MCP: run a prompt on your dataset, score each output 1-5 with an LLM judge.
These three are never merged into one score: opinions, measurements and popularity are different things.
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This address needs a key in a request header, which the add-connector menu cannot set. Use Claude Code, Cursor or VS Code instead.
Run this in your terminal
claude mcp add --transport http evals https://completionkit.com/mcp --header 'Authorization: your-value'Fill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"evals": {
"url": "https://completionkit.com/mcp",
"headers": {
"Authorization": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"evals": {
"type": "http",
"url": "https://completionkit.com/mcp",
"headers": {
"Authorization": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This address needs a key in a request header, which the add-connector menu cannot set. Use Claude Code, Cursor or VS Code instead.
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Tool names and descriptions exactly as the server reported them on Nov 10, 2026. The maker wrote them, not us; we show them as plain text.
prompts_listList all prompts
prompts_getGet a prompt by ID
prompts_createCreate a prompt
prompts_updateUpdate a prompt. If the prompt already has runs, this creates a new DRAFT version (current=false) rather than editing in place or publishing. Promote it with prompts_publish, so an agent's edits don't go live without a gate. If it has no runs, it is updated in place.
prompts_deleteDelete a prompt
prompts_publishPublish a prompt version, making it the current version
prompts_suggest_improvementSuggest an improved version of a prompt, grounded in a run's test results and judge feedback. Analyzes the run's responses, scores, and reviews, then returns reasoning plus a rewritten template (preserving {{variables}}) and persists it as a Suggestion. Requires a run that has a
runs_listList all runs
runs_getGet a run by ID, including "metric_averages": a per-metric breakdown with each metric's average score (or pass rate for checks), how many rows it graded, and how many scored low. Use this to find the metric dragging a prompt down without listing responses.
runs_createCreate a run. Omit prompt_id and provide output_column to score existing outputs by grading a pre-existing dataset column instead of generating new ones.
runs_updateUpdate a run
runs_deleteDelete a run
runs_generateStart a run. Required for every run, including score-only runs (no prompt): generates responses with the prompt when there is one, otherwise copies the graded dataset column and grades it.
runs_regradeRe-grade a run's existing responses with its currently attached metrics, without regenerating. Use after attaching or editing metrics on an already-generated run.
runs_rerunCreate and start a fresh copy of a run with the same prompt, dataset, metrics, and settings. Use when the judge changed and you want a clean run instead of mixing versions.
runs_retry_failuresRe-run only the failed responses of a run, optionally limited to specific response ids via "only".
responses_listList responses for a run, in row order. Returns {total, limit, offset, returned, responses}. Defaults to 50 rows because full payloads are large: use "fields" to drop the bodies, "min_score"/"max_score" to isolate low scorers, and sort "score_asc" to read the worst rows first. Fo
responses_getGet a specific response
datasets_listList all datasets
datasets_getGet a dataset by ID
datasets_createCreate a dataset with CSV data. First row is the header. Two column names are recognized specially: "expected_output" is each row's answer key (ground truth) given to the judge and to checks that compare against the row's expected value, and "actual_output" is a pre-made output t
datasets_updateUpdate a dataset
datasets_deleteDelete a dataset
datasets_create_from_urlCreate a dataset by downloading CSV from a URL instead of inlining it. Use this for large datasets: pass a public http(s) URL and the server fetches the CSV directly, so the data never has to pass through the tool-call arguments. The URL is SSRF-checked and the download is capped
metrics_listList all metrics
metrics_getGet a metric by ID
metrics_createCreate a metric with evaluation criteria. For a deterministic check set metric_type:"check" and check_config. Per-kind required keys: value (contains/not_contains/equals/list_overlap/numeric_equals), pattern (regex), json_path+expected (json_path_equals), min and/or max (length_b
metrics_updateUpdate a metric. For a deterministic check set metric_type:"check" and check_config. Per-kind required keys: value (contains/not_contains/equals/list_overlap/numeric_equals), pattern (regex), json_path+expected (json_path_equals), min and/or max (length_bounds/numeric_bounds); va
metrics_deleteDelete a metric
metrics_suggest_variantsAsk the model to rewrite the metric's judge instruction in N variants targeted at the recent disagreements. Each variant is saved as a draft MetricVersion with source="suggestion". Returns the persisted drafts. Stripe-metering hooks fire via ActiveSupport::Notifications under com
metric_groups_listList all metric groups
metric_groups_getGet a metric group by ID
metric_groups_createCreate a metric group
metric_groups_updateUpdate a metric group
metric_groups_deleteDelete a metric group
metric_versions_listList every MetricVersion (drafts + published) for a metric, newest first. Each row carries version_number, state, source, current flag, and timestamps.
metric_versions_publishPublish a MetricVersion as the live version of its metric. Works for both 'draft → published' and 'revert to an older published version → current'. Transactionally flips current, demotes peers, and writes the version's instruction + rubric_bands back onto the metric so the judge
metric_versions_dismissDestroy a draft MetricVersion (use for either source: 'edit' or source: 'suggestion'). Published versions are refused. To demote a published version, publish a different one as current instead.
provider_credentials_listList all provider credentials (API keys are not exposed)
provider_credentials_getGet a provider credential by ID (API key is not exposed)
provider_credentials_createCreate a provider credential
provider_credentials_updateUpdate a provider credential. Changing provider or api_endpoint on a credential with a stored key requires api_key in the same call, so a stored key never moves to a new provider or endpoint.
provider_credentials_deleteDelete a provider credential
tags_listList all tags
tags_getGet a tag by ID
tags_createCreate a tag. Color is auto-assigned.
tags_updateRename a tag.
tags_deleteDelete a tag. Removes the tag from every linked metric, prompt, run, and dataset.
agreements_listList agreements. Filter by run_id, response_id, metric_id, or created_by.
agreements_createUpsert an agreement for (run, response, metric, created_by). Verdict is one of agree, disagree, borderline. corrected_score (1..5) is required when verdict is 'disagree'.
judges_replayCreate a scoring run for the current judge over a dataset's existing outputs (wraps runs_create with prompt_id omitted and output_column supplied). This only sets up the run; call runs_generate to actually re-judge the outputs so you can compare against human verdicts.
judges_compareCompare two versions of one metric's agreement stats side by side. Requires metric_id, metric_version_a_id, and metric_version_b_id (both versions must belong to that metric). Unavailable for check metrics.
promptfoo_importImport a promptfooconfig.yaml. Creates a prompt, a dataset from the test vars, and metrics from the assert blocks (llm-rubric/g-eval become judge metrics; contains/equals/regex/is-json become deterministic check metrics). Returns a summary of what mapped and what was skipped and
usage_getGet this organization's plan usage and limits for the current billing period: runs and prompt fetches used, their limits, how many remain, and when the period resets. Call this to pre-check quota before starting runs. Runs are hard-blocked once the run limit is reached (with a sm
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 10, 2026
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It runs in the maker’s cloud. Connect over the internet with nothing to install, but what you send passes through the maker’s servers.
The GitHub repo last changed –, and the latest version (1.0.0) reached the registry 4 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.
In the same category: Context7, Codebase Memory, Exa
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Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
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Your prompts need tests too.
Run every prompt against real data. Score each output with an LLM judge against criteria you define. Change anything: the prompt, the model, the temperature, the dataset. Re-run and see exactly what got better and what broke. When the scores tell you something's off, CompletionKit suggests an improved prompt based on the judge's actual feedback on your runs. You inspect the diff, apply it as a new version, and verify the improvement.
It's the difference between "this prompt seems to work" and "this prompt scores 4.3 out of 5 across 200 inputs, up from 3.8 last version."
[Start on completionkit.com →](https://completionkit.com) | [RubyGems](https://rubygems.org/gems/completion-kit)
Just want to use it? CompletionKit Cloud is the same engine, fully hosted — zero install, no Rails ops, plans at completionkit.com/pricing.
Same engine, same UI, same REST API and MCP server — pick the deployment that fits. The first two are stack-agnostic: you run CompletionKit as a product and talk to it over HTTP and MCP, whatever language your own app is written in. The third is for teams already building on Rails.
The fastest path. Sign up and you're running on the same engine you'd self-host, without touching a Rails app. No db:migrate, no Puma, no Solid Queue, no provider key management — multi-tenant workspaces, your team logs in, you go. Plans at completionkit.com/pricing.
Run it on your own infra as a self-contained product. There's nothing to integrate and no Ruby to write — once it's up, you drive everything through the web UI, the REST API, and the MCP server, from whatever stack your own app is built in. It needs Postgres and a host that can run the app (Fly, Render, Heroku, Docker, …).
git clone https://github.com/homemade-software-inc/completion-kit.git
cd completion-kit/standalone bundle install bin/rails completion_kit:install:migrations bin/rails db:migrate
Run both a web server and a Solid Queue worker. In two terminals:
bin/rails server
bin/jobs
Or with foreman in one terminal: foreman start -f Procfile.dev.
Visit http://localhost:3000. Add a provider credential (Settings), create a prompt, upload a CSV dataset, and run it. See Deploying self-hosted for the production-env setup.
gem "completion-kit"
bin/rails generate completion_kit:install
bin/rails db:migrate
The engine mounts at /completion_kit. Generate / judge flows enqueue Active Job jobs (CompletionKit::GenerateRowJob, CompletionKit::JudgeReviewJob, CompletionKit::RunCompletionCheckJob), so your host app needs an Active Job adapter that actually processes them — Solid Queue, Sidekiq, GoodJob, etc. The :async adapter is not suitable for production: it runs jobs in the web Puma's thread pool with no durability and no retry, and a long LLM call will block request handling.
Host-app layout integration. If your host app overrides the engine layout (e.g. layout "application" on engine controllers, or rendering engine views inside your own shell), include both the engine's stylesheet and JavaScript in that layout:
<%= stylesheet_link_tag "completion_kit/application", media: "all" %>
<%= javascript_include_tag "completion_kit/application", defer: true, nonce: true %>
Without the JavaScript include, in-page behaviours silently fail: clickable table rows, dialogs, copy buttons, the API token reveal, the run form, live run status, live tag-breadcrumb updates, relative-time ticking, CSV row hover-expand, model-refresh progress, focus-first-error, and local-time formatting.
Content Security Policy. The engine's pages have no inline event handlers or inline scripts, and the engine layout puts the request nonce on its script tags, so the pages work under a nonce-based script-src. The views still use inline style attributes, so leave style-src out of the nonce directives and allow 'unsafe-inline' there for now.
Use a nonce that lasts the whole session, not a fresh random one per request. Turbo Drive re-creates the scripts in each new page body with the nonce from the csp-nonce meta tag, but the browser keeps enforcing the policy header of the first page it loaded, so a per-request nonce blocks nonce scripts after the first Drive visit.
Keep a random nonce in the session, and skip it for API controllers so cookie-less API and MCP calls get no session cookie, as the example below does. The Rails default, ->(request) { request.session.id.to_s }, also lasts the session, but it is empty on a brand-new visitor's first response, so a policy that admits scripts only by nonce blocks that first page's scripts until the session exists.
Rails.application.configure do
config.content_security_policy do |policy| policy.default_src :self policy.script_src :self policy.style_src :self, :unsafe_inline end
config.content_security_policy_nonce_generator = lambda do |request| next if request.controller_instance.is_a?(ActionController::API)
request.session[:csp_nonce] ||= SecureRandom.base64(16) end config.content_security_policy_nonce_directives = %w(script-src) end
CompletionKit discovers available models from each provider's API automatically.
| Provider | Env vars | What it covers |
|---|---|---|
| OpenAI | OPENAI_API_KEY | GPT-5, GPT-4.1, GPT-4o, etc. |
| Anthropic | ANTHROPIC_API_KEY | Claude Opus, Sonnet, Haiku |
| Ollama / local endpoint | OLLAMA_API_ENDPOINT (default: http://localhost:11434/v1), OLLAMA_API_KEY (optional) | Any model you've ollama pull-ed, or any OpenAI-compatible local server (vLLM, LM Studio, llama.cpp) |
| OpenRouter | OPENROUTER_API_KEY | 100+ models from 30+ providers through one API key |
| Azure AI Foundry | No env var — add via the Providers UI / a ProviderCredential (api_key + api_endpoint required, api_version optional) | Azure OpenAI deployments and Foundry projects |
Set these as environment variables or configure them in the generated initializer. You can also add provider credentials through the web UI under Settings.
The Ollama key from OLLAMA_API_KEY or config.ollama_api_key goes only to the Ollama endpoint from OLLAMA_API_ENDPOINT or config.ollama_api_endpoint, or to http://localhost:11434/v1 when neither is set and config.allow_loopback_endpoints is on, which is the default. A provider credential with no key of its own gets that key only while it points at that same endpoint. Point it anywhere else and CompletionKit sends no key, so store the key on the credential itself. A key stored on a credential always goes to that credential's endpoint. Two endpoints match when their scheme, host, port and path match. The case of the scheme and host, a default port written out, a trailing slash and a trailing /v1 make no difference. A blank stored key counts as no key.
Provider API keys are encrypted at rest using Active Record encryption. You need three encryption keys configured before the app will boot in production.
Generate them:
bin/rails db:encryption:init
Then set them as environment variables:
COMPLETION_KIT_ENCRYPTION_PRIMARY_KEY=<generated value>
COMPLETION_KIT_ENCRYPTION_DETERMINISTIC_KEY= COMPLETION_KIT_ENCRYPTION_KEY_DERIVATION_SALT=
Or add them to config/credentials.yml.enc under active_record_encryption. In development, the standalone app uses built-in fallback values so you can skip this step locally.
CompletionKit requires authentication in any deployed environment. In development and test, routes are open by default (with a log warning); every other environment returns 403 until auth is configured.
CompletionKit.configure do |c|
c.username = "admin" c.password = ENV["COMPLETION_KIT_PASSWORD"] end
CompletionKit.configure do |c|
c.auth_strategy = ->(controller) { controller.authenticate_user! } end
Only one mode can be active.
When someone records a verdict on a judge's score in the web UI, CompletionKit saves it under a reviewer name. Each reviewer keeps one verdict per response and metric, so give every person their own name. Otherwise their verdicts overwrite each other. Set current_reviewer to a callable that receives the controller and returns the signed-in person's name:
CompletionKit.configure do |c|
c.auth_strategy = ->(controller) { controller.authenticate_user! } c.current_reviewer = ->(controller) { controller.current_user&.email } end
The callable must return a String or nil. Any other value, such as a user record, raises CompletionKit::ConfigurationError. If current_reviewer is unset or returns a blank value, verdicts are saved under username, or "operator" when no username is set. The web UI does not read the X-Remote-User header on its own. If a proxy in front of your app sets that header, pass it through:
CompletionKit.configure do |c|
c.current_reviewer = ->(controller) { controller.request.headers["X-Remote-User"] } end
Only use this header when a trusted proxy always overwrites it. Otherwise any client can send it and claim any reviewer name.
The REST API and the MCP server take the reviewer name from their own created_by parameter.
The REST API, the MCP endpoint, and the web UI are rate limited per IP, per minute. The defaults are generous; tune them in the initializer:
CompletionKit.configure do |c|
c.api_rate_limit = 120 # REST API + MCP, requests per minute (default 120) c.web_rate_limit = 300 # web UI, requests per minute (default 300) end
Limiting uses Rails.cache. A shared cache store (Solid Cache, Redis) throttles accurately across multiple app instances; a per-process store still throttles each instance independently.
Uploads, prompt templates, and regex check patterns have limits, so one large or hostile input cannot tie up a worker. Tune them in the initializer:
CompletionKit.configure do |c|
c.max_upload_bytes = 25 * 1024 * 1024 # largest dataset CSV or import file (default 25 MB) c.max_template_bytes = 1024 * 1024 # largest prompt template (default 1 MB) c.regex_timeout = 1.0 # seconds a regex check may spend matching one row (default 1.0) end
A prompt whose template is over max_template_bytes fails validation when its template is saved. Prompts saved before a
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