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update_issue

Modify an existing GitLab issue's title, description, or state (close/reopen) using its project ID and issue IID.

Instructions

Update an issue.

Args:
    project_id: GitLab project ID
    issue_iid: Issue IID
    title: New title (optional)
    description: New description (optional)
    state_event: State change (close, reopen) (optional)
    token: GitLab Personal Access Token (optional)
    ctx: MCP context (automatically injected)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
issue_iidYes
titleNo
descriptionNo
state_eventNo
tokenNo
ctxNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The update_issue tool handler function. It updates a GitLab issue by making a PUT request to /projects/{project_id}/issues/{issue_iid} with optional title, description, and state_event parameters.
    @mcp.tool()
    async def update_issue(project_id: int, issue_iid: int, title: str = None, description: str = None, state_event: str = None, token: str = None, ctx=None) -> str:
        """Update an issue.
        
        Args:
            project_id: GitLab project ID
            issue_iid: Issue IID
            title: New title (optional)
            description: New description (optional)
            state_event: State change (close, reopen) (optional)
            token: GitLab Personal Access Token (optional)
            ctx: MCP context (automatically injected)
        """
        data = {}
        if title: data["title"] = title
        if description: data["description"] = description
        if state_event: data["state_event"] = state_event
        
        result = await make_gitlab_request(f"/projects/{project_id}/issues/{issue_iid}", "PUT", data, ctx=ctx, token=token)
        
        if isinstance(result, dict) and "error" in result:
            return f"Error updating issue: {result['error']}"
        
        return f"Issue updated: #{result['iid']} - {result['title']} ({result['state']})"
  • The @mcp.tool() decorator registers update_issue as an MCP tool with FastMCP.
    @mcp.tool()
  • The make_gitlab_request helper function used by update_issue to make HTTP PUT 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)}
Behavior3/5

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

No annotations are provided, so the description carries the burden. It mentions authentication via token and automatic ctx injection, but does not disclose whether updates are partial or full, or specify atomicity or permissions.

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 uses a clear docstring format with a brief line followed by an Args section. It is structured but slightly verbose; could be more concise.

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

Completeness4/5

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

Given the 7 parameters and existence of output schema, the description is fairly complete, covering each parameter. It does not explain return values, but the output schema covers that.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description compensates well by listing all parameters with explanations. However, it could be more precise about valid values for state_event.

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 an issue', which is a specific verb and resource. This distinguishes it from siblings like 'close_issue' (which only closes) and 'create_issue'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lists optional parameters like state_event for state changes, but does not explicitly guide when to use this tool vs alternatives like close_issue or reopen via state_event.

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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