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list_starred_projects

Retrieve all projects starred by a specific GitLab user. Requires only the user ID.

Instructions

List projects starred by a specific user.

Args:
    user_id: GitLab user ID
    token: GitLab Personal Access Token (optional)
    ctx: MCP context (automatically injected)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_idYes
tokenNo
ctxNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The `list_starred_projects` tool handler function. It takes a `user_id` (and optional `token` and `ctx`), calls GitLab API `/users/{user_id}/starred_projects`, and returns a formatted list of starred projects (up to 10). Registered with @mcp.tool() decorator on line 1179.
    @mcp.tool()
    async def list_starred_projects(user_id: int, token: str = None, ctx=None) -> str:
        """List projects starred by a specific user.
        
        Args:
            user_id: GitLab user ID
            token: GitLab Personal Access Token (optional)
            ctx: MCP context (automatically injected)
        """
        data = await make_gitlab_request(f"/users/{user_id}/starred_projects", ctx=ctx, token=token)
        
        if isinstance(data, dict) and "error" in data:
            return f"Error: {data['error']}"
        
        if not data:
            return "No starred projects found for this user."
        
        projects = []
        for project in data[:10]:
            projects.append(f"• {project['name']} ({project['path_with_namespace']}) - ID: {project['id']}")
        
        return "\n".join(projects)
  • Tool registration via @mcp.tool() decorator on the `list_starred_projects` async function.
    @mcp.tool()
  • The `make_gitlab_request` helper function used by `list_starred_projects` to make HTTP requests to the GitLab API.
    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)}
Behavior2/5

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

No annotations are provided, so the description must carry the full burden of behavioral transparency. It only mentions that the token is optional and ctx is auto-injected, but does not state whether the operation is read-only, whether pagination is supported, or any side effects. The existence of an output schema is not leveraged in the description.

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?

The description is extremely concise with a single-line purpose and a short list of parameters. Every sentence provides necessary information with no redundancy.

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?

The tool has an output schema, so return values are covered elsewhere. However, the description lacks information on pagination, ordering, or any limitations. It is minimally sufficient but leaves gaps for an agent to fully understand behavior.

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 adds meaning by explaining user_id as 'GitLab user ID', token as 'GitLab Personal Access Token (optional)', and ctx as 'MCP context (automatically injected)'. However, this only slightly extends beyond the parameter names; it does not provide constraints, formats, or examples.

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 lists projects starred by a specific user, specifying the verb 'List' and the resource 'projects starred by a specific user'. It is distinct from sibling tools like list_projects and list_user_projects, though it does not explicitly differentiate itself.

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 such as list_projects or star_project. It only lists parameters without context on intended use cases 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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