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JoseGarayar

MCP Employee API Server

by JoseGarayar

add_employee

Add new employee records to the database by providing name and age information. This tool enables AI assistants to create employee entries through the MCP Employee API Server.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
ageYes

Implementation Reference

  • main.py:55-62 (handler)
    The @mcp.tool() decorated function implementing the 'add_employee' tool. It makes a POST request to the /employees endpoint with the provided name and age, returning the API response.
    @mcp.tool()
    async def add_employee(name: str, age: int) -> dict:
        url = f"{URL_BASE}/employees"
        response = await make_request(url, "POST", {
            "name": name,
            "age": age
        })
        return response
  • main.py:13-41 (helper)
    Helper function used by the add_employee tool (and others) to make HTTP requests to the backend API with error handling.
    async def make_request(url: str, method: str = "GET", json_data: dict = None) -> dict[str, Any] | None:
        """Make a request to the API with proper error handling for all HTTP methods."""
        headers = {
            "User-Agent": USER_AGENT,
            "Accept": "application/json"
        }
        
        # Add Content-Type header for requests with JSON data
        if json_data is not None:
            headers["Content-Type"] = "application/json"
        
        async with httpx.AsyncClient() as client:
            try:
                if method.upper() == "GET":
                    response = await client.get(url, headers=headers, timeout=30.0)
                elif method.upper() == "POST":
                    response = await client.post(url, headers=headers, json=json_data, timeout=30.0)
                elif method.upper() == "PUT":
                    response = await client.put(url, headers=headers, json=json_data, timeout=30.0)
                elif method.upper() == "DELETE":
                    response = await client.delete(url, headers=headers, timeout=30.0)
                else:
                    raise ValueError(f"Unsupported HTTP method: {method}")
                
                response.raise_for_status()
                return response.json()
            except Exception:
                return None
Behavior1/5

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

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

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

Completeness1/5

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

Tool has no description.

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

Parameters1/5

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

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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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