typed-schema-demo

name	typed-schema-demo
description	Example skill — demonstrates zero-dependency JSON Schema derivation from Python dataclasses and type annotations (issue #242). Use as a reference when authoring typed handlers that should publish inputSchema / outputSchema without hand-writing JSON. Not intended for production use.
license	MIT
compatibility	Python 3.10+
metadata	{"dcc-mcp":{"dcc":"python","version":"1.0.0","layer":"example","search-hint":"structured schema, dataclass, json schema, inputSchema, outputSchema, typed handler, pydantic-free","tags":"example, schema, structured"}}

Typed Schema Demo (issue #242)

This skill demonstrates the dcc_mcp_core.schema helpers landed for issue #242: authors write a typed handler and tool_spec_from_callable derives both inputSchema and outputSchema from the annotations, with no dependency on pydantic, jsonschema, or attrs.

What to look at

• scripts/demo.py — one handler using a dataclass input and a dataclass output. The derived schemas are structurally compatible with pydantic's model_json_schema() so callers can swap in pydantic later without migrating agents or cached schemas.

How to wire it into a server

The demo module builds a ToolSpec that is ready for dcc_mcp_core._tool_registration.register_tools(server, [spec]). Inside an adapter (e.g. Maya/Blender), register it during bootstrap:

from dcc_mcp_core._tool_registration import register_tools
from typed_schema_demo.scripts.demo import spec

register_tools(server, [spec], dcc_name="python")

When the negotiated MCP session is 2025-06-18, the gateway publishes outputSchema alongside inputSchema so clients can validate the structuredContent payload our handler returns.