Typed Schema Demo (issue #242)
SkillDev toolsExample 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.
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Then ask your AI: use the Typed Schema Demo (issue #242) skill
What this skill tells your AI
The instructions your AI receives, as published by dcc-mcp/dcc-mcp-core in examples/skills/typed-schema-demo/SKILL.md and read by ahel’s review.
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'smodel_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.
Signals
- GitHub stars
- 48
- Forks
- 4
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
typed-schema-demo- Source
- github.com/dcc-mcp/dcc-mcp-core
github.com/dcc-mcp/dcc-mcp-core