MCP Tool Definition Quality Score (TDQS) Spec

MCP Tool Definition Quality Score (TDQS) Spec

TDQS (Tool Definition Quality Score) is an open framework and reference implementation for scoring how well MCP tool definitions communicate to AI agents, producing per-tool scores, written justifications, and a tiered grade usable in CI, registries, and developer workflows.

MCP Tool Definition Quality Score (TDQS) Spec is developer tools software teams evaluate for developer tools. Use this page to review pricing, integration signals, and the best alternatives before you commit.

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MCP Tool Definition Quality Score (TDQS) Spec

TDQS (Tool Definition Quality Score) is an open specification and reference implementation that measures how well MCP tool definitions communicate intent and usage to AI agents. It grades each tool description across six weighted dimensions, generates written justifications for every point, and aggregates results into a numeric score and tier (A–F) that teams can use in CI, registries, and release gating. The project provides multiple ways to run the scorer — a browser playground, a CLI for local/CI usage, and a hosted HTTP API — and produces shareable reports and SVG badges for README and review workflows.

An open framework for scoring how well an MCP tool definition communicates to an AI agent. Specification, reference implementation, CLI and hosted API.

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Key Features

Six-dimension rubric with written justifications

A model grades six weighted dimensions (Purpose Clarity, Usage Guidelines, Behavioral Transparency, Parameter Semantics, Conciseness & Structure, Contextual Completeness) from 1 to 5 and writes a justification for every point.

Deterministic preprocessing and hard gates

Deterministic code reads schema and annotations, applies hard gates for degenerate definitions (e.g., no description), and computes deterministic signals like parameter coverage and invocation cost before model grading.

Multiple runtimes: Playground, CLI, Hosted API

Reference implementation runs in a browser playground, via an npx CLI (npm/PyPI), or over the hosted HTTP API, producing consistent scores across all clients.

Per-tool scores, tiers, and shareable reports/badges

Each run produces a numeric score, a tier (A–F), written reasons, a permanent report page, and an SVG badge that can be added to READMEs and dashboards.

Server-level metrics and additional dimensions

In addition to per-tool dimensions, TDQS measures server-level qualities (Disambiguation, Naming Consistency, Tool Count Appropriateness, Completeness) and reports coherence and overall results.

Reference implementation & distribution

The scorer is published as packages on npm and PyPI and is used by the playground, CLI, and hosted API to ensure consistent behavior.

Pricing

Current pricing details are not available from the vendor source.

Use Cases

Release gating and CI

Use TDQS scores and the fail-under flag in CI pipelines to block releases when tool description quality falls below an acceptable tier.

Registry quality monitoring

Score and monitor tool definitions across a registry to identify descriptions needing improvement and to surface server-level coherence metrics.

Developer tooling and linting

Run the CLI locally or in pre-commit/CI to lint tool definitions, get actionable justifications, and enforce naming and completeness standards.

Research and calibration

Use the calibration corpus and the deterministic specification to study description defects and improve agent-tool selection in controlled experiments.

Integrations

GitHub (Sign in / Registry workflows)

Playground and registry workflows mention signing in with GitHub and integrating with registry/CI workflows.

CLI / CI

npx mcp-tdqs can be run locally or in CI to gate releases and lint definitions.

npm / PyPI

Reference implementation is published on npm and PyPI for easy installation and integration.

Hosted HTTP API

A hosted scorer API accepts submissions and returns reports (example endpoint shown: https://tdqs.dev/api/v1/reports).

Benefits

Provides a repeatable, shareable numeric measure of how well tool descriptions communicate to agents.
Generates written, actionable justifications that teams can act on to improve descriptions.
Integrates into developer workflows (CLI/CI) and registries, enabling automated gating and monitoring.
Reference implementation and consistent scoring across playground, CLI, and API reduce variance between environments.

Limitations

Hosted API rate limit: the hosted API is described as allowing thirty calls a day per account.
Model grading is limited to the description: 'a model grades only what the description adds beyond' schema and annotations, so issues in schema/annotations are not fixed by the grader.
Degenerate definitions short-circuit: definitions with no description or a name restated as a sentence are hard-gated and receive a floor score.

Frequently Asked Questions

No verified FAQs are available.

Getting Started

  1. 1 Try the browser playground: sign in with GitHub and paste a tools/list result to score it through the hosted API.
  2. 2 Use the CLI: run npx mcp-tdqs score with --command or --url flags to lint and score definitions locally or in CI (example: npx mcp-tdqs score --command "node ./dist/server.js" --fail-under B).
  3. 3 Use the Hosted API: submit definitions to the API endpoint (curl https://tdqs.dev/api/v1/reports -H 'Authorization: Bearer tdqs_…' -d @tools.json) and poll the generated report.

Support

Playground

Browser playground accessible from the site; sign in with GitHub to run scoring through the hosted API.

Docs

Links on the site reference CLI docs and API docs (the page includes 'Read the CLI docs' and 'Read the API docs').

CLI

The npx mcp-tdqs CLI runs locally and in CI; examples and flags are shown on the site.

API

Available: Yes
Documentation:

https://tdqs.dev/api/v1/reports

Rate Limits:

Thirty calls a day per account.

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