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Glama

draft_commit_message_local

Generate conventional commit messages from a staged diff using a local Ollama model, avoiding cloud costs for routine commits.

Instructions

Draft a conventional-style commit message from a diff using the local model.

Use for routine commits where the cloud model's analysis isn't needed — it is cheap and fast. Runs locally at no cloud cost. Returns a single commit message (subject plus optional body) as text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
diffYesA staged diff, e.g. the output of `git diff --staged`.
modelNoOllama model name to run, e.g. 'llama3.1' or 'qwen2.5-coder'. Omit to use the server's configured default model.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.1.3
    • addedInput schema / properties / diff / description
      Added value: +"A staged diff, e.g. the output of `git diff --staged`."
    • addedInput schema / properties / model / description
      Added value: +"Ollama model name to run, e.g. 'llama3.1' or 'qwen2.5-coder'. Omit to use the server's configured default model."
  2. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden. It discloses local execution, no cloud cost, and the exact return format (single commit message as text), which are important operational traits. It could be more explicit about model availability or failure modes, but for a drafting tool this is adequate.

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?

The description is two paragraphs, front-loaded with the main purpose and efficiently covering usage and output. There is minor redundancy between 'cheap and fast' and 'no cloud cost', but overall it is appropriately sized and well-structured.

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?

For a two-parameter tool with an existing output schema, the description covers the core purpose, usage context, and return type. It does not explicitly state when to prefer a cloud alternative, but the implicit guidance is sufficient for correct invocation.

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?

The schema already fully documents both parameters (diff and model) with 100% coverage. The description adds no parameter-specific detail beyond what the schema provides, so the baseline score of 3 is appropriate.

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 clearly states the specific action—drafting a conventional-style commit message from a diff—while explicitly distinguishing this local-model tool from the cloud-model alternative and from sibling tools like ask_local or summarize_local. The verb, resource, and style constraints are all present.

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?

It provides explicit guidance on when to use the tool ('routine commits where the cloud model's analysis isn't needed') and emphasizes the cost/latency benefits. However, it does not name the alternative tool directly or list explicit when-not-to-use cases beyond that implied exclusion, so it stops short of a 5.

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