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get_pipeline_jobs

Retrieve jobs for a GitLab pipeline by providing project ID and pipeline ID. Returns job details for the specified pipeline.

Instructions

Get jobs for a specific pipeline.

Args:
    project_id: GitLab project ID
    pipeline_id: Pipeline ID
    token: GitLab Personal Access Token (optional)
    ctx: MCP context (automatically injected)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
pipeline_idYes
tokenNo
ctxNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The get_pipeline_jobs tool handler function. Registered via @mcp.tool() decorator on line 513. Fetches jobs for a specific pipeline from the GitLab API endpoint /projects/{project_id}/pipelines/{pipeline_id}/jobs and formats them (name, status, stage).
    @mcp.tool()
    async def get_pipeline_jobs(project_id: int, pipeline_id: int, token: str = None, ctx=None) -> str:
        """Get jobs for a specific pipeline.
        
        Args:
            project_id: GitLab project ID
            pipeline_id: Pipeline ID
            token: GitLab Personal Access Token (optional)
            ctx: MCP context (automatically injected)
        """
        endpoint = f"/projects/{project_id}/pipelines/{pipeline_id}/jobs"
        data = await make_gitlab_request(endpoint, ctx=ctx, token=token)
        
        if isinstance(data, dict) and "error" in data:
            return f"Error: {data['error']}"
        if not data:
            return "No jobs found."
        
        jobs = []
        for job in data:
            jobs.append(f"• {job['name']}: {job['status']} (Stage: {job['stage']})")
        return "\n".join(jobs)
  • Registration of get_pipeline_jobs as a tool via the @mcp.tool() decorator on line 513. The mcp object is a FastMCP instance created on line 9.
    @mcp.tool()
    async def get_pipeline_jobs(project_id: int, pipeline_id: int, token: str = None, ctx=None) -> str:
  • The make_gitlab_request helper function used by get_pipeline_jobs to make API calls to GitLab. It handles authentication (token from parameter, context headers, or env var), constructs the URL, and makes async HTTP requests.
    async def make_gitlab_request(endpoint: str, method: str = "GET", data: dict = None, ctx=None, token: str = None) -> dict[str, Any] | None:
        """Make a request to GitLab API with proper error handling."""
        # Priority: 1. Explicit token parameter, 2. Context headers, 3. Environment variable
        
        # If no explicit token provided, try to get from context
        if not token and ctx and hasattr(ctx, 'request_context') and ctx.request_context:
            # Try to get from request headers
            if hasattr(ctx.request_context, 'headers'):
                token = ctx.request_context.headers.get('GITLAB_TOKEN')
        
        # Fallback to environment variable
        if not token:
            token = os.getenv("GITLAB_TOKEN")
        
        if not token:
            return {"error": "GitLab token not provided. Please provide a token parameter, GITLAB_TOKEN in the request headers, or set the environment variable."}
        
        # Get GitLab URL (from context or environment)
        gitlab_url = os.getenv("GITLAB_URL", "https://gitlab.com")
        
        headers = {
            "PRIVATE-TOKEN": token,
            "Content-Type": "application/json"
        }
        
        url = f"{gitlab_url}/api/v4{endpoint}"
        
        async with httpx.AsyncClient() as client:
            try:
                if method == "GET":
                    response = await client.get(url, headers=headers, timeout=30.0)
                elif method == "POST":
                    response = await client.post(url, headers=headers, json=data, timeout=30.0)
                elif method == "PUT":
                    response = await client.put(url, headers=headers, json=data, timeout=30.0)
                elif method == "DELETE":
                    response = await client.delete(url, headers=headers, timeout=30.0)
                
                response.raise_for_status()
                return response.json() if response.content else {"success": True}
            except Exception as e:
                return {"error": str(e)}
Behavior1/5

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

No annotations provided, so the description bears full burden. It does not disclose behavioral traits such as read-only nature, authentication requirements (token optional but not explained), error handling, rate limits, or pagination. The description is too minimal.

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 short and front-loaded with the main purpose. The args list could be seen as redundant given the schema, but it provides some explanatory value for token and ctx. Overall efficient with no unnecessary sentences.

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

Completeness2/5

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

Given the complexity (4 params, 2 required, output schema exists), the description is incomplete. It lacks context on what jobs are, if there are limits, filtering options, or any notes on permissions or rate limits. The agent lacks sufficient context for confident 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 coverage is 0%, so description must compensate. It adds meaning for 'token' (GitLab Personal Access Token, optional) and 'ctx' (automatically injected), but project_id and pipeline_id are just repeated without additional context. Minimal value added beyond the schema.

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 'Get jobs for a specific pipeline,' which is a specific verb and resource. It distinguishes from siblings like get_pipelines that retrieve pipelines rather than jobs. No tautology.

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?

No guidance on when to use this tool versus alternatives. It does not mention when not to use it or suggest sibling tools like get_pipelines for listing pipelines. The implied usage is only that you need a pipeline ID.

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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