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jolfr

Commit Helper MCP

by jolfr

get_commit_questions

Generate conventional commit messages by answering interactive questions that guide proper formatting and structure.

Instructions

Get interactive questions for commit message generation.

Returns: Dict containing the questions and metadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The core handler function for the 'get_commit_questions' MCP tool. Decorated with @mcp.tool() for registration and @handle_errors for error handling. Retrieves commit questions from the CommitzenService, formats the response with count and plugin info, and returns a success response.
    @mcp.tool()
    @handle_errors(log_errors=True)
    def get_commit_questions() -> Dict[str, Any]:
        """
        Get interactive questions for commit message generation.
    
        Returns:
            Dict containing the questions and metadata
        """
        questions = service.get_questions()
    
        result = {
            "questions": questions,
            "count": len(questions),
            "plugin": service.get_info().get("plugin_name"),
        }
    
        return create_success_response(result)
  • Import statement in mcp_server.py that brings in the get_commit_questions tool function, triggering its @mcp.tool() decorator registration when the module is imported.
    from .server.workflow_tools import (
        get_commit_questions,
        health_check,
        refresh_configuration,
        commit_workflow_step,
    )
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions the return type ('Dict containing the questions and metadata'), which adds some behavioral context, but fails to disclose key traits like whether it's read-only, requires authentication, has rate limits, or what 'interactive' implies (e.g., user input needed). This is inadequate for a tool with no annotation coverage.

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 concise with two sentences: one stating the purpose and another specifying the return type. It's front-loaded with the main function, and each sentence adds value without waste. However, it could be slightly more structured by integrating the return info into the first sentence for better flow.

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

Completeness3/5

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

Given the tool has 0 parameters, 100% schema coverage, and an output schema exists (implied by 'Returns'), the description is somewhat complete. It covers the purpose and return type, but lacks context on when to use it among siblings and behavioral details like interactivity requirements. For a simple retrieval tool, this is minimally adequate but has clear gaps in guidance and transparency.

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?

The tool has 0 parameters, and schema description coverage is 100%, so there's no need for parameter details in the description. The baseline for zero parameters is 4, as the description doesn't need to compensate for any gaps. It appropriately avoids redundant parameter information.

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

Purpose3/5

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

The description states the tool 'Get interactive questions for commit message generation,' which clarifies its purpose as retrieving questions. However, it's vague about what 'interactive questions' entail and doesn't differentiate from siblings like 'get_commit_types' or 'smart_commit_suggestion,' which might also provide guidance for commits. It avoids tautology by not just restating the name, but lacks specificity in distinguishing its role.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives. The description doesn't mention context, prerequisites, or exclusions, such as whether it's for initial commit setup or iterative refinement. With siblings like 'create_commit_message' and 'generate_commit_message,' the lack of usage guidelines leaves the agent unsure of when this tool is appropriate.

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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