Skip to main content
Glama

get_project_languages

Retrieve programming languages used in a GitLab project by providing the project ID.

Instructions

Get programming languages used in a project.

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
tokenNo
ctxNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function for the 'get_project_languages' tool. It calls the GitLab API endpoint /projects/{project_id}/languages, parses the returned language-percentage pairs, and formats them as a string listing each language with its percentage.
    @mcp.tool()
    async def get_project_languages(project_id: int, token: str = None, ctx=None) -> str:
        """Get programming languages used in a project.
        
        Args:
            project_id: GitLab project ID
            token: GitLab Personal Access Token (optional)
            ctx: MCP context (automatically injected)
        """
        data = await make_gitlab_request(f"/projects/{project_id}/languages", ctx=ctx, token=token)
        
        if isinstance(data, dict) and "error" in data:
            return f"Error: {data['error']}"
        
        if not data:
            return "No language data available."
        
        languages = []
        for lang, percentage in data.items():
            languages.append(f"• {lang}: {percentage:.1f}%")
        
        return "\n".join(languages)
  • The tool is registered via the @mcp.tool() decorator, which makes it available as an MCP tool named 'get_project_languages'.
    @mcp.tool()
    async def get_project_languages(project_id: int, token: str = None, ctx=None) -> str:
  • Helper function used by get_project_languages to make HTTP requests to the GitLab API. Handles authentication token resolution, URL construction, and error handling.
    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 fails to disclose behavioral details such as authentication requirements, error handling, or rate limits. Only mentions that token is optional and ctx is auto-injected.

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 very concise, with a clear purpose statement and brief parameter explanations. No unnecessary information.

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?

For a simple tool with an output schema, the description covers basic purpose and parameter meaning but lacks usage guidance and behavioral context. It is minimally adequate.

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?

The description adds meaning to parameters (e.g., token is a Personal Access Token) beyond the schema, but does not fully compensate for the 0% schema description coverage. Details like project_id format are missing.

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 retrieves programming languages used in a project, but does not explicitly differentiate it from sibling tools like get_project or get_project_branches.

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 is provided on when to use this tool versus alternatives, nor any conditions or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/skmprb/gitlab-clone-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server