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🔌 Agent Integrations & Protocols

MCP & AI Function Calling Schema Generator

Build and test parameter schemas for Model Context Protocol (MCP) servers, OpenAI tools, Anthropic functions, TypeScript interfaces, and Python Pydantic models.

Quick Preset Tool Templates:

1. Tool Definition & Metadata

2. Parameters / Input Schema (3)

#1
#2
#3
Generated Tool Schema
{
  "name": "search_web",
  "description": "Performs a live web search using search engine index and returns ranked snippet results.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "query": {
        "type": "string",
        "description": "The search query or terms to look up"
      },
      "max_results": {
        "type": "integer",
        "description": "Maximum number of search results to return (1-20)",
        "enum": [
          "5",
          "10",
          "20"
        ]
      },
      "search_depth": {
        "type": "string",
        "description": "Depth of search algorithm",
        "enum": [
          "basic",
          "advanced"
        ]
      }
    },
    "required": [
      "query"
    ]
  }
}

Standardizing Tool Interfaces Across AI Platforms

As autonomous agents and coding assistants become standard, defining robust tool interfaces is essential. Whether exposing a local database query to an MCP server or providing web search to GPT-4o, standardized JSON schemas ensure reliable type validation.

Supported Target Formats

Frequently Asked Questions

What is Model Context Protocol (MCP) and how do tool schemas work?

Model Context Protocol (MCP) is an open standard developed by Anthropic that allows AI applications and agents to connect securely to local files, tools, and enterprise data sources. Tool schemas define the parameters and types expected by an MCP server tool.

Which schema formats can I generate with this tool?

You can generate Model Context Protocol (MCP) JSON schemas, OpenAI Function Calling tool specs, Anthropic Tool definitions, TypeScript parameter interfaces, and Python Pydantic V2 models.

Why is a clear parameter description important for LLM tool calling?

LLMs decide whether and how to invoke external functions based entirely on the parameter names, types, and descriptions provided in the schema. Clear descriptions drastically reduce tool call hallucination.

Is this generator free to use for production projects?

Yes, 100% free with no limits, no sign-ups, and no server uploads.

How do Enum choices help steer LLM tool arguments?

Providing an Enum constraint (e.g. 'celsius' or 'fahrenheit' for temperature units) restricts the model to only selecting predefined valid values, preventing invalid arguments from causing runtime errors.

How do I implement MCP in Python or Node.js?

Use the official '@modelcontextprotocol/sdk' package in TypeScript/Node.js or 'mcp' in Python. You register tools with the exact JSON input schema generated by this tool.