What this error means

MCP connector model intermittently emits tool_use instead of mcp_tool_use for tools declared in mcp_toolset — conversation stalls on client side is a Anthropic API failure pattern reported for developers trying to debug why anthropic sdk with mcp beta returns plain tool_use instead of expected mcp_tool_use, causing agent loops to hang. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub Issue anthropics/anthropic-sdk-python#1581 (open, created 2026-05-21, 1 comment, 2605 related MCP/Auth issues in claude-code repo). Affects claude-sonnet-4-6 and other 4.x models under streaming API. Intermittent bug with multi-turn agentic loops mixing local function tools + mcp_toolset. This causes API billing charges for failed turns plus developer time wasted debugging. Category mapping: Anthropic SDK/MCP errors → Anthropic API per SKILL.md category table.

Common causes

  • GitHub Issue anthropics/anthropic-sdk-python#1581 (open, created 2026-05-21, 1 comment, 2605 related MCP/Auth issues in claude-code repo). Affects claude-sonnet-4-6 and other 4.x models under streaming API. Intermittent bug with multi-turn agentic loops mixing local function tools + mcp_toolset. This causes API billing charges for failed turns plus developer time wasted debugging. Category mapping: Anthropic SDK/MCP errors → Anthropic API per SKILL.md category table.

Quick fixes

  1. Confirm the exact error signature matches MCP connector model intermittently emits tool_use instead of mcp_tool_use for tools declared in mcp_toolset — conversation stalls on client side.
  2. Check the Anthropic API account, local tool state, and provider configuration involved in the failing workflow.
  3. Compare the failing environment with a known working setup, then change one configuration value at a time.

Platform/tool-specific checks

  • Verify the command, editor, extension, or API client that produced the error.
  • Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
  • Avoid deleting credentials, local model data, or project settings until the failing scope is clear.

Step-by-step troubleshooting

  1. Capture the exact error message and the command, editor action, or request that triggered it.
  2. Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
  3. Review the source evidence below and compare it with your environment.
  4. Apply one change at a time and rerun the smallest failing action.
  5. Keep the working fix documented for the team or deployment environment.

How to prevent it

  • Keep provider/tool configuration documented.
  • Record non-secret diagnostics such as tool version, provider name, model name, and command path.
  • Add a lightweight check before CI or production workflows depend on the tool.