Hacktoberfest 2026: as issues que os mantenedores marcaram para outubro, abertas e boas para iniciantes. Ver issues do Hacktoberfest

func_metadata raises uncaught PydanticSchemaGenerationError for Iterator/AsyncIterator tool return annotations instead of the unstructured fallback

Aberta Para iniciantes
#3,573 2 comentários 0 reações 0 responsáveis Ver no GitHub

Ninguém assumiu esta issue ainda.

Avaliação

Dificuldade
2/5
Tempo estimado
1-3 horas
Facilidade para iniciantes
65/100
Tipo de issue
Bug
Clareza
Claramente especificada
Status de atividade
Ativa
Stack de tecnologia
python
Domínio
api, backend

Direção de pesquisa

O problema está em src/mcp/server/mcpserver/utilities/func_metadata.py. Veja a linha 444 onde _create_output_model é chamado fora do bloco try/except. A correção é mover essa chamada para dentro do bloco try/except existente que captura PydanticUserError e pydantic_core.SchemaError. Teste com o script de reprodução fornecido para garantir que os tipos de retorno Iterator/AsyncIterator agora ou recorram à saída não estruturada ou levantem InvalidSignature conforme documentado.

Escrita pelo modelo de indexação a partir do texto da issue.

Descrição

v1 v2
Initial Checks
Release line

2.x (current stable)

Description

Registering a tool whose function is annotated -> Iterator[...] or -> AsyncIterator[...] — the PEP 484 spelling for generator functions — raises a raw pydantic.errors.PydanticSchemaGenerationError at registration time, instead of either falling back to an unstructured tool (structured_output=None, the default) or raising the SDK's InvalidSignature (structured_output=True). The same happens via @server.tool() / Tool.from_function(), so a properly typed generator tool cannot be registered at all.

Actual output of the script in "Example Code" (error text truncated at 80 chars by the script):

func_metadata(search): UNCAUGHT pydantic.errors.PydanticSchemaGenerationError: Unable to generate pydantic-core schema for typing.Iterator[str]. Set `arbitrary
func_metadata(search, structured_output=True): UNCAUGHT pydantic.errors.PydanticSchemaGenerationError: Unable to generate pydantic-core schema for typing.Iterator[str]. Set `arbitrary
Tool.from_function(search): UNCAUGHT pydantic.errors.PydanticSchemaGenerationError: Unable to generate pydantic-core schema for typing.Iterator[str]. Set `arbitrary

Full traceback (captured with the collections.abc spelling of the same annotation, so the error names it accordingly):

  File "src/mcp/server/mcpserver/utilities/func_metadata.py", line 444, in func_metadata
    output_model, wrap_output = _create_output_model(original_annotation, return_type_expr, func.__name__)
  File "src/mcp/server/mcpserver/utilities/func_metadata.py", line 550, in _create_output_model
    model = _create_wrapped_model(func_name, original_annotation)
  File "src/mcp/server/mcpserver/utilities/func_metadata.py", line 621, in _create_wrapped_model
    return create_model(model_name, result=annotation)
pydantic.errors.PydanticSchemaGenerationError: Unable to generate pydantic-core schema for collections.abc.Iterator[str]. Set `arbitrary_types_allowed=True` in the model_config to ignore this error or implement `__get_pydantic_core_schema__` on your type to fully support it.

What I expected is what already happens for other unserializable return types, pinned by test_structured_output_unserializable_type_error (tests/server/mcpserver/test_func_metadata.py:1233, passes on main) and documented in docs/servers/structured-output.md: with structured_output=None, registration succeeds and output_schema is None (fallback to text); with structured_output=True, InvalidSignature: Function search: return type ... is not serializable for structured output. For contrast, Iterable[str] and Generator[str, None, None] both register successfully through the same wrapped-model path — only the PEP 484-recommended spellings for generators crash.

Root cause: _create_output_model(...) is called at src/mcp/server/mcpserver/utilities/func_metadata.py:444, outside the try/except at lines 446–470 whose except tuple (PydanticUserError, pydantic_core.SchemaError, ...) exists exactly so that "an unsupported return type surfaces here, at registration" as a clean failure. _create_output_model_create_wrapped_modelcreate_model(model_name, result=annotation) (line 621) builds a schema itself, and its PydanticSchemaGenerationError (a PydanticUserError subclass) escapes uncaught. Moving the line 444 call inside the existing try/except looks like it would restore both documented behaviours; I'd be happy to be assigned and open a PR with that approach.

Related: the guard was added in #2434 (for #1131), but it wraps only the FuncMetadata construction, not this call. #1060 reports the same error class for a different type (Image, 1.x fastmcp) and looks unrelated to this code path.

AI disclosure: this issue and its reproduction were prepared with AI assistance.

Example Code
from typing import Iterator

from mcp.server.mcpserver.tools import Tool
from mcp.server.mcpserver.utilities.func_metadata import func_metadata


def search(n: int) -> Iterator[str]:
    yield from ["a"] * n


for label, call in [
    ("func_metadata(search)", lambda: func_metadata(search)),
    ("func_metadata(search, structured_output=True)", lambda: func_metadata(search, structured_output=True)),
    ("Tool.from_function(search)", lambda: Tool.from_function(search)),
]:
    try:
        call()
        print(f"{label}: OK")
    except Exception as e:
        print(f"{label}: UNCAUGHT {type(e).__module__}.{type(e).__name__}: {str(e)[:80]}")
# AsyncIterator[str] return annotations behave identically
Python & MCP Python SDK
mcp: main @ 6affe5c0d3588fd1705713b3703dc68015cfe3eb
     func_metadata.py is identical in v2.2.0 (latest 2.x release);
     the same crash reproduces on a fresh `pip install mcp==2.2.0`
Python 3.13.15
pydantic 2.12.5
macOS 26.6.2 (arm64)
Linguagem predominante
Python
Estrelas
24.3k
Forks
4k
Merge médio
1d 16h
PRs com merge (30d)
25

Guia de contribuição

Abrir o guia de contribuição

Primeiros passos

  1. Leia a issue inteira e depois o guia de contribuição do projeto.
  2. Comente na issue dizendo que vai assumir — evita que duas pessoas façam o mesmo trabalho.
  3. Faça um fork do repositório e trabalhe em uma branch.
  4. Abra um pull request que referencie o número da issue.

Mais de modelcontextprotocol/python-sdk

Todas as issues de modelcontextprotocol/python-sdk

Issues semelhantes

Mais issues de Python

Receba novas issues na sua caixa de entrada

Um resumo curto de issues do GitHub para quem está começando.