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

[bot] Anthropic: MCP connector tool calls (`mcp_tool_use`/`mcp_tool_result`) are silently dropped from tool spans

Aberta Para iniciantes
#797 0 comentários 0 reações 0 responsáveis Ver no GitHub

Mantenedores costumam responder em até 1 dia

Ninguém assumiu esta issue ainda.

Avaliação

Dificuldade
2/5
Tempo estimado
1-3 horas
Facilidade para iniciantes
75/100
Tipo de issue
Bug
Clareza
Claramente especificada
Status de atividade
Ativa
Stack de tecnologia
python

Direção de pesquisa

O problema está em py/src/braintrust/integrations/anthropic/tracing.py. Comece lendo a função _log_server_tool_spans por volta da linha 1455 e as definições de _SERVER_TOOL_USE_TYPE e _is_server_tool_result_type. A correção é estender a verificação do lado da chamada para também corresponder a "mcp_tool_use". Certifique-se de que os blocos mcp_tool_result sejam corretamente emparelhados com suas chamadas. Execute os testes existentes para a integração Anthropic para verificar a correção.

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

Descrição

Summary

Anthropic's MCP connector feature (tools=[{"type": "mcp_toolset", ...}] on client.messages.create(), gated by the mcp-client-2025-11-20 beta header) returns mcp_tool_use and mcp_tool_result content blocks when Claude calls a remote MCP server's tools. The repo's Anthropic tracing does not recognize mcp_tool_use as a tool call at all, so:

  • MCP tool calls are never captured into a span (no input, no tool name, no call metadata).
  • MCP tool results are still detected (because the result-type check is suffix-based) but are logged as orphaned spans with no call context, since the corresponding call was never registered.

This is a correctness gap, not just missing-coverage: half of each MCP tool exchange is dropped and the other half is logged incompletely/incorrectly.

What is missing

In py/src/braintrust/integrations/anthropic/tracing.py:

_SERVER_TOOL_USE_TYPE = "server_tool_use"          # line 1337

def _is_server_tool_result_type(item_type: Any) -> bool:   # line 1340
    return isinstance(item_type, str) and item_type.endswith("_tool_result") and item_type != "tool_result"

_log_server_tool_spans (line 1455) pairs calls and results by walking response content:

item_type = item.get("type")
if item_type == _SERVER_TOOL_USE_TYPE:      # line 1470 — only matches "server_tool_use"
    ...
    continue

if not _is_server_tool_result_type(item_type):   # line 1481
    continue
  • The call-side check only matches the literal string "server_tool_use" (used for built-in server tools like web search / code execution). It does not match "mcp_tool_use", so mcp_tool_use blocks fall through the loop entirely and are never added to calls_by_id.
  • The result-side check (_is_server_tool_result_type) matches anything ending in _tool_result except the literal tool_result, so mcp_tool_result does pass this check — but since no matching call was ever registered, it's appended as (None, item), producing a tool span with output only and no input/tool name.

Separately, _MANAGED_AGENTS_CALL_TYPES (line 905) does include "agent.mcp_tool_use" — but that's the distinct, agent.-prefixed type used by the Managed Agents API (client.beta.agents/client.beta.sessions), not the plain mcp_tool_use/mcp_tool_result types returned by the standard Messages API's MCP connector.

Braintrust docs status: unclear / not_found

The Anthropic integration page mentions mcp_servers exactly once, as one of many request parameters captured in span metadata for the Go SDK's request-param list. It does not mention mcp_toolset, mcp_tool_use, or mcp_tool_result anywhere, and does not document any tool-span behavior specific to the MCP connector for any language. The only documented span-splitting for server-side tools is generic ("server-side tool calls ... appear as child tool spans"), described for the Java SDK, and is not confirmed to apply to MCP connector blocks specifically.

Upstream sources

  • Anthropic MCP connector docs: https://platform.claude.com/docs/en/agents-and-tools/mcp-connector — confirms the mcp_toolset tool type, the mcp-client-2025-11-20 beta header, and the exact response content block types "mcp_tool_use" and "mcp_tool_result" (example response blocks shown verbatim in the "How MCP connector tool calls work" section).

Local repo files inspected

  • py/src/braintrust/integrations/anthropic/tracing.py (full file, 1627 lines) — specifically _SERVER_TOOL_USE_TYPE (line 1337), _is_server_tool_result_type (line 1340), _log_server_tool_spans (line 1455), _MANAGED_AGENTS_CALL_TYPES (line 905)
  • py/src/braintrust/integrations/anthropic/integration.py
  • py/src/braintrust/integrations/anthropic/patchers.py
Linguagem predominante
Python
Estrelas
20
Forks
18
Merge médio
1d 1h
PRs com merge (30d)
82

Preparar o ambiente

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 braintrustdata/braintrust-sdk-python

Todas as issues de braintrustdata/braintrust-sdk-python

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.