# TDQS

> An open framework for scoring how well an MCP tool definition communicates to an AI agent.

- **Website:** https://tdqs.dev
- **Pricing:** unknown
- **Categories:** Developer Tools, AI Agents
- **Tags:** developer-tools, ai-agents, writing-content
- **Platforms:** Web, CLI
- **Last verified:** 2026-09-10
- **Canonical page:** https://linkrena.com/tools/tdqs

## About

The description is the only thing an agent reads before it picks a tool. TDQS grades every definition on six weighted dimensions, explains each point, and turns the result into a tier you can gate a release on — the same numbers in your terminal, in CI, and across Glama's registry.

Code extracts what the schema and annotations already say; a model grades only what the description adds beyond them. Every stage of the pipeline is specified, and the deterministic ones are exact to the digit across implementations.

Deterministic code reads the schema and annotations: parameter coverage, output schema, behavioural hints, invocation cost, and a hash so the same definition always scores the same.

Degenerate definitions — no description, a name restated as a sentence — short-circuit with a flag and a floor score. No model call is spent on them.

A model grades six dimensions from 1 to 5 against published anchors, and writes a justification for every point. The prompts are the specification, byte for byte.

Overrides, flags and smells are applied by code, the weights roll up in integer arithmetic, and the result is a score, a tier, and the reasons.

Six weighted dimensions grade each tool; four more, equally weighted, judge the server as a whole. Every score comes with a written reason, and every dimension has a page with its anchors.

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