Hacktoberfest 2026: die Issues, die Maintainer für den Oktober markiert haben – offen und einsteigerfreundlich. Hacktoberfest-Issues durchsuchen

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

Offen Anfängerfreundlich
#3,573 2 Kommentare 0 Reaktionen 0 zugewiesene Personen Auf GitHub ansehen

Dieses Issue hat noch niemand übernommen.

Bewertung

Schwierigkeit
2/5
Geschätzter Aufwand
1-3 Stunden
Anfängerfreundlichkeit
65/100
Issue-Typ
Bug
Klarheit
Klar beschrieben
Aktivitätsstatus
Aktiv
Tech-Stack
python
Bereich
api, backend

Rechercherichtung

Das Problem liegt in src/mcp/server/mcpserver/utilities/func_metadata.py. Siehe Zeile 444, wo _create_output_model außerhalb des try/except-Blocks aufgerufen wird. Die Lösung besteht darin, diesen Aufruf in den bestehenden try/except-Block zu verschieben, der PydanticUserError und pydantic_core.SchemaError abfängt. Testen Sie mit dem bereitgestellten Reproduktionsskript, um sicherzustellen, dass Iterator/AsyncIterator-Rückgabetypen nun entweder auf unstrukturierte Ausgabe zurückfallen oder InvalidSignature wie dokumentiert auslösen.

Vom Indexierungsmodell aus dem Issue-Text verfasst.

Beschreibung

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)
Vorherrschende Sprache
Python
Sterne
24.3k
Forks
4k
Ø Merge
1 T. 16 Std.
Gemergte PRs (30 T.)
25

Beitragsleitfaden

Beitragsleitfaden öffnen

Erste Schritte

  1. Lesen Sie das ganze Issue und danach den Beitragsleitfaden des Projekts.
  2. Schreiben Sie ins Issue, dass Sie es übernehmen — das erspart doppelte Arbeit.
  3. Forken Sie das Repository und arbeiten Sie in einem Branch.
  4. Öffnen Sie einen Pull Request, der die Issue-Nummer nennt.

Mehr aus modelcontextprotocol/python-sdk

Alle Issues in modelcontextprotocol/python-sdk

Ähnliche Issues

Weitere Issues zu Python

Neue Issues direkt in Ihr Postfach

Eine kurze Übersicht über anfängerfreundliche GitHub-Issues.