Add a runtime create_model variant to the schema_validators example#3148
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Aaron-Oh wants to merge 2 commits into
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Add a runtime create_model variant to the schema_validators example#3148Aaron-Oh wants to merge 2 commits into
Aaron-Oh wants to merge 2 commits into
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The story showed four ways to type a tool parameter (BaseModel, TypedDict, dataclass, dict). Add a fifth: a pydantic model built at runtime with create_model from an external JSON Schema dict, then handed to @mcp.tool() like any BaseModel. Covers the doc gap behind issues modelcontextprotocol#323, modelcontextprotocol#761, modelcontextprotocol#772. A create_model() result is opaque to static type checkers, so a TYPE_CHECKING branch aliases it to a same-shape declared model while the runtime uses the dynamic class. server_lowlevel.py, client.py and README.md are updated to include the new variant.
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Re-trigger cubic
- server.py: JSON Schema 'required' is optional, so default it to an empty list before membership testing. External schemas with only optional properties no longer raise KeyError at import time. - README.md: greet_dynamic publishes the same schema as greet_pydantic, so the client bullet now says four typed variants, not three.
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Thanks for the review — addressed both in 230f718:
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What
The
schema_validatorsexample story shows four ways to type a toolparameter so
MCPServerderives and enforcesinputSchema: a pydanticBaseModel, aTypedDict, a@dataclass, and a baredict[str, Any].This adds a fifth variant: a pydantic model built at runtime with
create_modelfrom an external JSON Schema dict, then handed to@mcp.tool()exactly like a hand-writtenBaseModel.Why
Issues #323, #761, and #772 all asked the same thing: how to drive a
tool's
inputSchemafrom a JSON Schema you already hold (from OpenAPI, aconfig file, a DB row) rather than a class written out in source. The
maintainer answer is "use a pydantic model as the parameter" — but the
example suite never showed how to get that model when it isn't declared
statically. This closes that documentation gap with a runnable variant.
Notes
create_model()result is opaque to static type checkers (its fieldsdon't exist until runtime, and a runtime variable can't appear in a type
annotation). A
TYPE_CHECKINGbranch aliases it to a same-shape declaredmodel so type checkers can see the fields; at runtime the dynamic class is
what
@mcp.tool()reflects over. This is called out in the README.greet_pydanticvariant — thepoint is purely how the model is obtained, not a different wire shape.
server_lowlevel.py,client.py, andREADME.mdare updated to coverthe new variant.
Validation
uv run --frozen ruff format --check/ruff check— cleanuv run --frozen pyright— 0 errorsuv run --frozen pytest tests/examples -k schema— 14 passed(in-memory/http × modern/legacy × server/server_lowlevel, plus the
manifest and story-shape checks)