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list_user_projects

Retrieve all projects owned by a given GitLab user by providing their user ID.

Instructions

List projects owned 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_user_projects` tool handler function. It takes a `user_id` (int), optional `token` and `ctx`, calls the GitLab API endpoint `/users/{user_id}/projects`, and returns a formatted list of projects (up to 10).
    @mcp.tool()
    async def list_user_projects(user_id: int, token: str = None, ctx=None) -> str:
        """List projects owned 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}/projects", ctx=ctx, token=token)
        
        if isinstance(data, dict) and "error" in data:
            return f"Error: {data['error']}"
        
        if not data:
            return "No 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)
  • The tool is registered via the `@mcp.tool()` decorator on line 1155, which automatically registers `list_user_projects` with the FastMCP instance.
    @mcp.tool()
  • The input schema is defined via the function signature and docstring: `user_id: int` is required, `token: str = None` and `ctx` are optional.
    async def list_user_projects(user_id: int, token: str = None, ctx=None) -> str:
        """List projects owned by a specific user.
        
        Args:
            user_id: GitLab user ID
            token: GitLab Personal Access Token (optional)
            ctx: MCP context (automatically injected)
        """
  • The `make_gitlab_request` helper function is used by `list_user_projects` to make HTTP requests to the GitLab API. It handles authentication via token (from parameter, context headers, or environment variable) and returns JSON responses.
    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 exist, so the description must carry the burden. It implies a read-only operation but omits details on authentication requirements beyond an optional token, rate limits, pagination, or any side effects.

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 to-the-point, using a docstring format. It front-loads the purpose and lists arguments efficiently. A slight improvement could be omitting the args list if redundant with schema, but it adds clarity.

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 simplicity (list tool, has output schema), the description covers basic purpose and parameters. However, it misses contextual details like pagination behavior, error conditions, or the default behavior when token is omitted.

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 the description adds value by briefly explaining each parameter (user_id, token optional, ctx auto-injected). However, it lacks constraints, formats, or examples beyond basic identifications.

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 action (list), the resource (projects), and the scope (owned by a specific user). It distinguishes from siblings like 'list_projects' and 'list_group_projects' by the user filter, though not explicitly.

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 like 'list_projects' or 'list_group_projects'. No exclusions or context are given.

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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