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cdmx-in
by cdmx-in

get_user

Retrieve specific user details from the Goodday project management platform by providing a user ID for secure context-aware applications.

Instructions

Get details of a specific user.

Args: user_id: The ID of the user to retrieve

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_idYes

Implementation Reference

  • The handler function for the 'get_user' MCP tool. It fetches user details from the Goodday API endpoint 'user/{user_id}' and formats the response using format_user.
    @mcp.tool()
    async def get_user(user_id: str) -> str:
        """Get details of a specific user.
    
        Args:
            user_id: The ID of the user to retrieve
        """
        data = await make_goodday_request(f"user/{user_id}")
        
        if not data:
            return "User not found."
        
        if isinstance(data, dict) and "error" in data:
            return f"Unable to fetch user: {data.get('error', 'Unknown error')}"
        
        return format_user(data)
  • Helper function used by get_user to format the raw user data dictionary into a human-readable string.
    def format_user(user: dict) -> str:
        """Format a user into a readable string with safe checks."""
        if not isinstance(user, dict):
            return f"Invalid user data: {repr(user)}"
    
        # Defensive defaults in case nested keys are not dicts
        role = user.get('role') if isinstance(user.get('role'), dict) else {}
    
        return f"""
    User ID: {user.get('id', 'N/A')}
    Name: {user.get('name', 'N/A')}
    Email: {user.get('email', 'N/A')}
    Role: {role.get('name', 'N/A')}
    Status: {user.get('status', 'N/A')}
    """.strip()
  • Helper function to fetch all users and create a mapping of user IDs to names, potentially useful for resolving user names in tool outputs.
    async def get_user_mapping() -> dict:
        """Get mapping of user IDs to names."""
        data = await make_goodday_request("users")
        user_id_to_name = {}
        if isinstance(data, list):
            for u in data:
                if isinstance(u, dict):
                    user_id_to_name[u.get("id")] = u.get("name", "Unknown")
        return user_id_to_name
  • MCP tool registration decorator for the get_user function.
    @mcp.tool()
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves user details, implying a read-only operation, but does not disclose any behavioral traits such as authentication requirements, rate limits, error handling, or what specific details are returned. This is a significant gap for a tool with no annotation coverage.

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 appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by a structured 'Args' section for parameters. There is no wasted text, and the structure enhances readability. However, it could be slightly more concise by integrating the parameter explanation into the main description.

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's complexity (a read operation with one parameter), lack of annotations, and no output schema, the description is incomplete. It does not explain what details are returned (e.g., user name, email, role), potential errors, or how it differs from sibling tools like 'get_users'. For a tool with no structured data support, more contextual information is needed.

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?

The description adds meaningful context for the single parameter: 'user_id: The ID of the user to retrieve.' With schema description coverage at 0% (the schema only provides a title 'User Id' and type 'string'), the description compensates by explaining the parameter's purpose. Since there is only one parameter, the baseline is high, and the description adequately clarifies its semantics.

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's purpose: 'Get details of a specific user.' It specifies the verb ('Get') and resource ('user'), but does not distinguish it from sibling tools like 'get_users' (which likely retrieves multiple users) or 'get_project_users' (which might retrieve users within a project context). The purpose is clear but lacks sibling differentiation.

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 sibling tools such as 'get_users' (for listing users) or 'get_project_users' (for users in a project), nor does it specify prerequisites or exclusions. Usage is implied only by the tool name and description, with no explicit context.

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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