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lemenkov
by lemenkov

get_pull_request

Retrieve detailed pull request information including comments from Pagure git forges to review changes and manage code contributions.

Instructions

Get detailed information about a specific pull request.

Args: project: Project name pr_id: Pull request ID number namespace: Project namespace (default: rpms)

Returns: JSON string with PR details including comments

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectYes
pr_idYes
namespaceNorpms

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Actual implementation of the pull request retrieval logic in the API client.
    async def get_pull_request(
        self,
        project: str,
        pr_id: int,
        namespace: str = "rpms",
    ) -> PagurePullRequest:
        """Get pull request details.
    
        Args:
            project: Project name
            pr_id: Pull request ID
            namespace: Project namespace
    
        Returns:
            PagurePullRequest model
        """
        response = await self.client.get(
            f"{self.api_base}/{namespace}/{project}/pull-request/{pr_id}",
            headers=self._get_headers(),
        )
        response.raise_for_status()
        data = response.json()
        return PagurePullRequest(**data)
  • MCP tool registration and wrapper function for get_pull_request.
    async def get_pull_request(
        project: str,
        pr_id: int,
        namespace: str = "rpms",
    ) -> str:
        """Get detailed information about a specific pull request.
    
        Args:
            project: Project name
            pr_id: Pull request ID number
            namespace: Project namespace (default: rpms)
    
        Returns:
            JSON string with PR details including comments
        """
        client = get_client()
        result = await client.get_pull_request(project, pr_id, namespace)
    
        return result.model_dump_json(indent=2)
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 of behavioral disclosure. It states the tool returns 'JSON string with PR details including comments,' which hints at read-only behavior and output format, but doesn't cover critical aspects like authentication needs, rate limits, error handling, or whether it's idempotent. For a tool with no annotations, this leaves significant gaps in understanding its operational traits.

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 appropriately sized and front-loaded, starting with the core purpose. The 'Args' and 'Returns' sections add structure without verbosity. However, the 'Returns' section could be more precise (e.g., specifying JSON structure), and some sentences like 'JSON string with PR details including comments' are slightly redundant with the purpose statement, but overall it's efficient with minimal waste.

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's moderate complexity (3 parameters, no annotations, but has an output schema), the description is partially complete. It covers the basic purpose and parameters, and the output schema likely details return values, reducing the need for description there. However, it lacks usage guidelines and full behavioral context, making it adequate but with clear gaps for effective agent use.

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 0%, so the description must compensate. It lists parameters (project, pr_id, namespace) with brief explanations, adding meaning beyond the bare schema (e.g., 'Pull request ID number' clarifies pr_id as numeric, and default for namespace). However, it doesn't fully detail semantics like format constraints or examples, leaving some ambiguity. With 3 parameters and low schema coverage, this provides moderate but incomplete compensation.

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?

The description clearly states the tool's purpose: 'Get detailed information about a specific pull request.' It specifies the verb ('Get') and resource ('pull request'), making the action explicit. However, it doesn't differentiate from sibling tools like 'list_pull_requests' or 'get_project_info' beyond mentioning 'specific' pull request, which is somewhat implied by the required pr_id parameter.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_pull_requests' for listing multiple PRs or 'get_project_info' for broader project details, nor does it specify prerequisites or contexts for usage. The only implied usage is when detailed info on a specific PR is needed, but this is basic and lacks explicit alternatives or exclusions.

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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