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delimit_tdqs_lint

Score Python @mcp.tool() docstrings against the 6 TDQS dimensions to catch low-quality descriptions before publishing your MCP server.

Instructions

Score MCP tool docstrings against the 6 TDQS dimensions (LED-2108).

When to use: as a CI gate before publishing the MCP server, to catch low-quality tool descriptions. Operates on any Python file with @mcp.tool()-decorated functions.

When NOT to use: for runtime tool selection or policy decisions — TDQS grades documentation, not behaviour. Use delimit_lint for OpenAPI specs and delimit_gov_evaluate for policy-class decisions.

Sibling contrast: unlike delimit_lint (OpenAPI specs) and delimit_spec_health (spec quality scoring), this scores Python source against Glama's Tool Definition Quality Score rubric.

Side effects: none. Pure read-only static analysis via ast (no import, no execution). Does not write ledger, evidence, or notify.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
target_fileNoPath to a Python file with @mcp.tool() decorators. Default "ai/server.py", resolved against cwd.ai/server.py
humanNoIf True, include a human-readable "report" string in the response. Default False (JSON-only is cheaper for CI pipes).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses side effects as none, states it uses static analysis via ast (no import, no execution), and does not write ledger, evidence, or notify. Full transparency with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured: opening sentence, usage guidelines, sibling contrast, side effects. No wasted words, front-loaded with core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has an output schema, description suffices without explaining return format. Covers purpose, usage, parameters, side effects, and context for a static analysis tool. Complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%. Description adds practical context: default paths, resolution, and when to use the human flag for CI pipes. Adds value beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool scores MCP tool docstrings against TDQS dimensions. Specifies the scope (Python files with @mcp.tool() decorators) and distinguishes from siblings like delimit_lint and delimit_spec_health.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use (as a CI gate before publishing MCP server) and when not to use (runtime tool selection or policy decisions). Contrasts with siblings to guide selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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