What AI agents actually do with your MCP server

说明与用途

MCP 服务器看似正常，但代理会遇到隐藏的错误，如缺失工具、参数不匹配和低效响应。mcpspan 发现了五个问题，可通过修复来改善代理交互。

问题

MCP servers appear healthy but agents encounter hidden failures such as missing tools, argument mismatches, and inefficient responses.

结果

After instrumentation, five hidden failure patterns were uncovered and addressed, leading to clearer agent interactions and reduced error rates.

工作流

• Instrument the server with mcpspan
• Run simulated traffic
• Collect metrics on tool calls, arguments, and response sizes
• Analyze patterns of missing tools, argument errors, description issues, looping calls, and large responses
• Apply fixes: rename tools, adjust descriptions, add limits or summaries
• Re‑measure to confirm improvements

证据

Agents guess tool names. Some guesses are near misses: searchFlights when the tool is search_flights, seatMap instead of seat_map. Others are a tool the agent assumed should exist, like get_flight_price.

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When an agent sends arguments that don't match the input schema, the MCP SDK rejects them before your handler runs and sends the validation error back to the model as a normal result.

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The fix was one sentence in the description. The dotted line on the chart is where it changed: Errors drop right at the line, while the code didn't change at all.

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An agent tries check_in, gets "Check-in opens 24 hours before departure", and calls check_in again with exactly the same arguments.

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A tool that usually answers with 6 kB and sometimes with 650 kB looks fine on every latency chart, because it's fast. But that answer lands in the agent's context window, crowds out everything else, and costs tokens on every following turn.

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