git:20260505.5c57b27 to git:20260927.f5cfb64

---

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:

```python

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.