diff --git a/pyproject.toml b/pyproject.toml index 0c474da..6a82dd7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "langchain-mcp" -version = "0.1.0a1" +version = "0.1.0" description = "Model Context Protocol tool calling support for LangChain" readme = "README.md" authors = [ diff --git a/src/langchain_mcp/toolkit.py b/src/langchain_mcp/toolkit.py index 8abaac6..dc0da0a 100644 --- a/src/langchain_mcp/toolkit.py +++ b/src/langchain_mcp/toolkit.py @@ -49,10 +49,35 @@ def get_tools(self) -> list[BaseTool]: ] +TYPEMAP = { + "integer": int, + "number": float, + "array": list, + "boolean": bool, + "string": str, + "null": type(None), +} + +FIELD_DEFAULTS = { + int: 0, + float: 0.0, + list: [], + bool: False, + str: "", + type(None): None, +} + + +def configure_field(name: str, type_: dict[str, t.Any], required: list[str]) -> tuple[type, t.Any]: + field_type = TYPEMAP[type_["type"]] + default_ = FIELD_DEFAULTS.get(field_type) if name not in required else ... + return field_type, default_ + + def create_schema_model(schema: dict[str, t.Any]) -> type[pydantic.BaseModel]: # Create a new model class that returns our JSON schema. # LangChain requires a BaseModel class. - class Schema(pydantic.BaseModel): + class SchemaBase(pydantic.BaseModel): model_config = pydantic.ConfigDict(extra="allow") @t.override @@ -62,7 +87,14 @@ def __get_pydantic_json_schema__( ) -> JsonSchemaValue: return schema - return Schema + # Since this langchain patch, we need to synthesize pydantic fields from the schema + # https://github.com/langchain-ai/langchain/commit/033ac417609297369eb0525794d8b48a425b8b33 + required = schema.get("required", []) + fields: dict[str, t.Any] = { + name: configure_field(name, type_, required) for name, type_ in schema["properties"].items() + } + + return pydantic.create_model("Schema", __base__=SchemaBase, **fields) class MCPTool(BaseTool): diff --git a/uv.lock b/uv.lock index bc39466..a7bd13c 100644 --- a/uv.lock +++ b/uv.lock @@ 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