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Read-only

Find commits by matching words in their messages to answer questions like when a change was introduced or which commit fixed an issue.

Instructions

Find commits by what their messages say. Use this to answer "when did we change X?" or "which commit fixed Y?". For the history of one function or file, use explain_history instead.

Input Schema

NameRequiredDescriptionDefault
kNoHow many results to return, 1 to 25.
repoNoName of an indexed repo, as list_repos shows it. Leave out when only one repo is indexed.
queryYesWords from commit messages, e.g. "fix timezone" or "drop python 3.8".

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.2.1
    • addedInput schema / properties / k / description
      Added value: +"How many results to return, 1 to 25."
    • addedInput schema / properties / query / description
      Added value: +"Words from commit messages, e.g. \"fix timezone\" or \"drop python 3.8\"."
    • addedInput schema / properties / repo / description
      Added value: +"Name of an indexed repo, as list_repos shows it. Leave out when only one repo is indexed."
  2. First observedv0.2.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds meaningful behavioral context by scoping the match to commit message text rather than diffs or code, which prevents a likely misuse; it does not discuss ranking or result ordering, but the output schema covers the return shape.

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 short sentences, each earning its place: scope, use cases, and the redirect to an alternative. The key disambiguation is placed after the core purpose but nothing is buried or padded.

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?

For a simple three-parameter read-only search with a fully documented schema and an output schema, the description covers purpose, usage, and the nearest competing tool. An agent has everything needed to call it correctly.

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

Parameters3/5

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

Schema description coverage is 100%, with k, repo, and query all documented including defaults and the single-repo case, so the schema carries the parameter burden. The description reinforces the query semantics ('words from commit messages') with an example but adds no syntax or format detail beyond what the schema already states.

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?

The description opens with a specific verb and resource -- 'Find commits by what their messages say' -- which immediately separates it from code-content search. It also explicitly names the sibling tool explain_history as the one to use for a different scope, so the agent can discriminate without opening schemas.

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?

It supplies two concrete natural-language questions this tool answers ('when did we change X?', 'which commit fixed Y?') and an explicit when-not rule routing function/file history to explain_history. Both the use case and the alternative are stated, leaving nothing to inference.

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