modelcontextprotocol / modelcontextprotocol/python-sdk
Parameterized Context loses request state in resource and prompt handlers
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Descrição
Description
A resource-template or prompt handler annotated with a parameterized Context[LifespanContextT] receives a different Context object from the one the server injected. The reconstructed object has none of the private request state, so accessing ctx.request_context, ctx.session, ctx.request_id, or lifespan state fails with:
ValueError: Context is not available outside of a request
The equivalent tool handler works, and handlers annotated with unparameterized Context happen to work. This makes the documented typed lifespan-context pattern unusable specifically in dynamic resources and prompts.
Minimal reproduction
from collections.abc import AsyncIterator
from contextlib import asynccontextmanager
import anyio
from mcp.client import Client
from mcp.server.mcpserver import Context, MCPServer
@asynccontextmanager
async def lifespan(_: MCPServer[str]) -> AsyncIterator[str]:
yield "live-state"
async def main() -> None:
server = MCPServer("probe", lifespan=lifespan)
@server.resource("probe://{name}")
async def resource(name: str, ctx: Context[str]) -> str:
return f"{name}:{ctx.request_context.lifespan_context}"
@server.prompt("probe")
async def prompt(name: str, ctx: Context[str]) -> str:
return f"{name}:{ctx.request_context.lifespan_context}"
async with Client(server) as client:
await client.read_resource("probe://value")
await client.get_prompt("probe", {"name": "value"})
anyio.run(main)
Both calls fail. Each should return content containing value:live-state.
Root cause
ResourceTemplate.from_function and Prompt.from_function correctly omit the detected context parameter from their public argument schemas, but then wrap the original handler with pydantic.validate_call. At invocation time they inject the live Context into the wrapped handler, so Pydantic validates it again.
For a generic annotation, Pydantic converts the unparameterized runtime instance into Context[str, Any]. That creates a new model instance and does not carry over Context's private _request_context, _mcp_server, and related attributes:
server-created Context
|
v
validate_call parameter: Context[str]
|
v
new Context[str, Any] instance
|
v
private request state absent
A direct identity probe on current main shows:
Context -> same object, request state present
Context[str] -> new object, request state absent
Tools avoid this because FuncMetadata.call_fn_with_arg_validation validates only client-supplied arguments and passes injected values directly to the raw handler.
Impact
This affects resource templates and prompts that use typed lifespan context, including otherwise valid code following the SDK's generic Context[LifespanContextT] typing. It can also hide in tests that only assert that ctx is non-null rather than reading request-scoped state.
A compatible fix should preserve the existing validation/coercion of client-supplied resource or prompt arguments while passing the server-created context object directly, with its identity and private state intact.
Environment
- MCP Python SDK: 2.0.0 and current
mainata4f4ccd091138771535e17191123f20b30fda68e - Python: 3.12.13
- Pydantic: 2.12.5
AI assistance was used to investigate and draft this report.
Guia de contribuição
Primeiros passos
- Leia a issue inteira e depois o guia de contribuição do projeto.
- Comente na issue dizendo que vai assumir — evita que duas pessoas façam o mesmo trabalho.
- Faça um fork do repositório e trabalhe em uma branch.
- Abra um pull request que referencie o número da issue.
Direção de pesquisa
Comece em ResourceTemplate.from_function e Prompt.from_function e compare o caminho de validação deles com FuncMetadata.call_fn_with_arg_validation usado por tools. Reproduza as chamadas de resource e prompt do exemplo mínimo e verifique se o Context injetado mantém sua identidade e o estado da requisição. Está concluído quando os argumentos do cliente continuam sendo validados, enquanto o Context criado pelo servidor chega inalterado aos dois handlers.
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Avaliação
- Stack de tecnologia
- python
- Domínio
- backend-api-design
- Tipo de issue
- Bug
- Dificuldade
- 4/5
- Tempo estimado
- 3-5 dias
- Status de atividade
- Pouca atividade
- Clareza
- Claramente especificada
- Facilidade para iniciantes
- 55/100