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update_project

Modify GitLab project settings by providing the project ID and optional changes to name, description, or visibility.

Instructions

Update project settings.

Args:
    project_id: GitLab project ID
    name: New project name (optional)
    description: New description (optional)
    visibility: New visibility (private, internal, public) (optional)
    token: GitLab Personal Access Token (optional)
    ctx: MCP context (automatically injected)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
nameNo
descriptionNo
visibilityNo
tokenNo
ctxNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The 'update_project' tool handler - an async function decorated with @mcp.tool() that updates a GitLab project's name, description, and/or visibility via PUT request to /projects/{project_id}.
    @mcp.tool()
    async def update_project(project_id: int, name: str = None, description: str = None, visibility: str = None, token: str = None, ctx=None) -> str:
        """Update project settings.
        
        Args:
            project_id: GitLab project ID
            name: New project name (optional)
            description: New description (optional)
            visibility: New visibility (private, internal, public) (optional)
            token: GitLab Personal Access Token (optional)
            ctx: MCP context (automatically injected)
        """
        data = {}
        if name: data["name"] = name
        if description: data["description"] = description
        if visibility: data["visibility"] = visibility
        
        result = await make_gitlab_request(f"/projects/{project_id}", "PUT", data, ctx=ctx, token=token)
        
        if isinstance(result, dict) and "error" in result:
            return f"Error updating project: {result['error']}"
        
        return f"Project updated: {result['name']} (ID: {result['id']})"
  • Input schema/definition for 'update_project' - defines parameters: project_id (required int), name (optional str), description (optional str), visibility (optional str), token (optional str), ctx (auto-injected context).
    async def update_project(project_id: int, name: str = None, description: str = None, visibility: str = None, token: str = None, ctx=None) -> str:
        """Update project settings.
        
        Args:
            project_id: GitLab project ID
            name: New project name (optional)
            description: New description (optional)
            visibility: New visibility (private, internal, public) (optional)
            token: GitLab Personal Access Token (optional)
            ctx: MCP context (automatically injected)
        """
  • The @mcp.tool() decorator on line 662 registers 'update_project' as an MCP tool with the FastMCP server instance.
    @mcp.tool()
  • The 'make_gitlab_request' helper function used by update_project to make the PUT request 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?

With no annotations, the description must disclose behavioral traits. It only states 'Update project settings,' which implies mutation but lacks details on authentication, error handling, side effects, or constraints. The token parameter hints at auth but is not explained.

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 a single sentence and a parameter list. The main action is front-loaded. However, the parameter list essentially mirrors the schema, which could be streamlined.

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 tool is a mutation with an output schema, the description omits important context such as prerequisites (project must exist), permissions needed, and potential side effects. The presence of an output schema does not excuse the lack of behavioral completeness.

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 provides minimal added value for most parameters (e.g., repeating names from schema) but crucially adds allowed values for 'visibility' (private, internal, public) not present in the schema. This partial compensation prevents a lower score.

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 'Update project settings,' specifying the verb 'Update' and the resource 'project settings.' It effectively distinguishes from sibling tools like create_project, delete_project, and archive_project.

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 does not mention when not to use it, nor does it reference sibling tools like create_project or archive_project for alternative tasks.

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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