AI agent tool calls failing or returning wrong data.Fixed this session.

PM assigned in 10 minutes. MCP engineer starts diagnosing within the hour. Tool return value issues fall into a small number of patterns: the tool is returning data in a format Claude cannot parse or use effectively (returning a raw database row object instead of a formatted string, or returning a list when a single value is expected), the tool is silently catching exceptions and returning an empty result instead of an error that Claude can react to, the tool is executing correctly but the result is being truncated by a context window constraint, or the tool schema description is misleading Claude about what the tool returns causing it to misinterpret the output. The engineer traces the exact tool execution using MCP Inspector, identifies the failure pattern, and fixes the tool handler and schema.

Tool selection confusion is a prompt and schema design problem. Claude selects tools based on the tool name, description, and the overlap between the tool parameters and the current user request. When tools have similar names or descriptions, Claude uses the one whose description best matches the current context but if the descriptions are too similar, it makes wrong selections. The engineer fixes this by: making tool names more distinctive (a tool named search is too generic; search_product_database vs. search_knowledge_base is unambiguous), improving descriptions to be specific about when to use each tool (include the exact scenarios where this tool applies and scenarios where it does not), and adding few-shot examples to the system prompt demonstrating correct tool selection for the types of requests that are currently misrouted.

Yes. Robust retry logic for MCP tools is a standard reliability improvement. The engineer implements: exponential backoff retry for transient failures (network timeouts, rate limits, temporary API unavailability) with a configurable maximum retry count, idempotency handling so retried tool calls do not create duplicate side effects (calling a write tool twice should not create two database records), circuit breaker pattern that stops retrying a specific tool after N consecutive failures and returns a clear error to Claude rather than consuming the entire context window with retry attempts, and structured error responses that include whether the failure is retriable or permanent so Claude can make the right decision about next steps.

MCP Inspector is the primary debugging tool it provides a web interface to connect to your MCP server and invoke tools directly, showing the full request and response JSON, error details, and execution time without needing a Claude client. The engineer sets up MCP Inspector in your development environment and uses it to reproduce the exact failing tool call, inspect the raw request and response, and iterate on the fix without waiting for a full Claude client interaction cycle. For production debugging, the engineer adds structured logging to every tool invocation (tool name, input parameters, execution time, output summary, and any errors) integrated with your existing observability platform. The logs enable you to replay specific failing tool calls and diagnose issues from production behaviour without needing to reproduce them locally.

Six requirements that distinguish a production MCP tool from a prototype: first, complete input validation (every required parameter validated against the schema, helpful error messages for missing or invalid inputs rather than unhandled exceptions); second, comprehensive error handling (the tool returns structured MCP errors for all failure modes rather than crashing or returning empty results); third, idempotency for write operations (calling the tool twice with the same parameters does not create duplicate side effects); fourth, rate limiting and timeout configuration (the tool enforces time limits on external API calls and respects the rate limits of downstream services); fifth, structured logging for every invocation (tool name, input parameters, execution time, success/failure, and output summary for debugging and cost tracking); sixth, graceful handling of large outputs (tools that might return large datasets implement pagination rather than attempting to return everything in a single response that overflows the context window).

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