AI Agent Used 437,000 Tokens to Answer a Simple Question via MCP Server

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Developers at FoxNose ran 18 real-world test scenarios connecting AI agents from Anthropic and OpenAI to their MCP server, uncovering severe token inefficiency in how agents handle tool calls. A single straightforward question triggered 20 consecutive tool calls, consuming 437,000 input tokens — because each new call re-sent the entire conversation history to the model. The core problem was not server errors or protocol failures, but bloated responses returning far more data than agents needed per call. The team addressed this by capping text field output at 1,000 characters with truncation markers and reducing the default page size to 5 results, cutting token usage in one scenario from 437,000 to 78,000. Their findings highlight that optimizing round-trip efficiency and response size matters far more than model selection when deploying AI agents in production.

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