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run_verification

Execute repository tests under coverage and map results to requirements. Filter by requirement IDs to run only linked tests.

Instructions

Run the repo's tests under coverage and map results to requirements. Python: pytest with per-test contexts. C/C++: builds with clang coverage (and MC/DC) using the build commands in .reqtrace.yaml, then runs each test in its own process. With req_ids, runs only tests linked to those requirements. The commands come from the repo's config, never from arguments.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
req_idsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
run_idYes
outcomesYes
timed_outYes
duration_sYes
output_tailYesTail of test output, for diagnosing errors
failed_testsYes
requirement_statusYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false and openWorldHint=false, so the bar is lower. The description still adds real value: it discloses that commands are sourced from .reqtrace.yaml and 'never from arguments', and that C/C++ tests each run in their own process, conveying an expensive build+instrument+execute operation. It does not say what artifacts are written, which is the only notable gap.

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

Conciseness4/5

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

Four short sentences, front-loaded with the core action and then progressively adding language-specific detail. No filler. Slightly dense with implementation detail (per-test contexts, MC/DC) that could be trimmed, but each clause is informative.

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

Completeness4/5

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

An output schema exists, so return-value explanation is unnecessary, and the description covers invocation mechanics, language branching, and the config-sourced command constraint. The remaining omission is runtime/artifact side effects, which matters for a non-read-only tool but is not critical to correct invocation.

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 description coverage is 0% for the single req_ids parameter, so the description must carry the load — and it does ('With req_ids, runs only tests linked to those requirements'), clarifying that it is a filter rather than a selector of tests to execute. It stops short of saying whether values are requirement IDs in the same format used by get_requirement/list_requirements.

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

Purpose4/5

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

States a concrete verb+resource ('Run the repo's tests under coverage and map results to requirements') and specifies per-language mechanics. It is clearly distinct from read-only siblings like coverage_report or trace_matrix, but it never names an alternative, so the differentiation is left implicit.

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

Usage Guidelines3/5

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

The req_ids clause gives one usable condition ('runs only tests linked to those requirements'), which implies the scoped vs unscoped workflow. However, there is no explicit when-not-to-use guidance and no pointer to sibling tools such as coverage_report when the agent only wants existing data rather than a fresh run.

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