Generate from MCP Tool Schemas¶

Generate Python models from Model Context Protocol (MCP) tool schema profiles.

Quick Start¶

datamodel-codegen \
    --input tools-list.json \
    --input-file-type mcp-tools \
    --output-model-type pydantic_v2.BaseModel \
    --output model.py

Supported Input Shapes¶

The mcp-tools input type accepts:

• a tools/list JSON-RPC response containing result.tools;
• an MCP server definition containing a top-level tools array;
• a single tool definition or an array of tool definitions;
• JSON Schema documents whose $defs or definitions values are tool definitions.

Each tool must define name and inputSchema. If outputSchema is present, it is generated too.

Example¶

tools-list.json

{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "tools": [
      {
        "name": "search",
        "inputSchema": {
          "type": "object",
          "properties": {
            "query": { "type": "string" }
          },
          "required": ["query"]
        },
        "outputSchema": {
          "type": "object",
          "properties": {
            "results": {
              "type": "array",
              "items": { "type": "string" }
            }
          },
          "required": ["results"]
        }
      }
    ]
  }
}

Generated model.py

from __future__ import annotations

from pydantic import BaseModel


class SearchInput(BaseModel):
    query: str


class SearchOutput(BaseModel):
    results: list[str]

Naming¶

Tool names are converted to PascalCase and suffixed with Input or Output.

For example:

Notes¶

MCP inputSchema and outputSchema entries are converted into JSON Schema definitions before generation. Local $defs and definitions entries inside a tool schema are hoisted and prefixed to avoid collisions between tools.

See Also¶

• MCP Tools specification
• MCP Schema reference
• Generate from JSON Schema