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PatchLoop

by varad-oss
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Python
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run_tests

Run the repository's test suite or a targeted pytest selector to return a pass/fail summary and truncated failure output; reproduce bugs before edits and verify fixes after changes.

Instructions

Run the repository's test suite (or a subset) and return a pass/fail summary plus the failure output, truncated. selector is passed to pytest, e.g. 'tests/test_x.py::test_name' or '-k median'. Run this to reproduce the bug first and again after every edit.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "run_tests"
  }
}

Input Schema

NameRequiredDescriptionDefault
selectorNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior3/5

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

With no annotations, the description carries the burden and does disclose useful behavior: failures are returned truncated and selector is passed to pytest. However, it omits execution traits an agent would want, such as timeouts, working directory, environment assumptions, or side effects of running the suite.

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?

Three tight sentences, front-loaded with what the tool does, then the parameter semantics, then the usage cue. Every sentence adds information without redundancy.

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 values need not be fully explained, and the description still notes the pass/fail summary and truncated failures. Combined with the parameter explanation it is nearly complete, with only execution-environment and timeout details missing.

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

Parameters5/5

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

Schema coverage is 0%, so the description must compensate and does: it explains that selector is passed to pytest and gives concrete examples ('tests/test_x.py::test_name' or '-k median'). This turns a bare optional string into a usable parameter.

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?

States a specific verb and resource ('Run the repository's test suite (or a subset)') and specifies what is returned: a pass/fail summary plus truncated failure output. This is clearly distinguishable from siblings like bash, which would run arbitrary commands, since this tool is test-suite-specific.

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

Usage Guidelines4/5

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

Explicitly says when to use it: 'Run this to reproduce the bug first and again after every edit,' which gives a concrete workflow context. It does not explicitly rule out using bash for the same purpose, so the alternative selection is implied rather than stated.

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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