| 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.
