How to Fix an AI Agent's Tool Descriptions Without Guessing

Start from the trace, not from a generic prompt rewrite. A tool-description candidate is plausible when the agent selected an available but inappropriate tool, skipped the appropriate tool, or supplied arguments that match the wrong tool's contract. It is not the right first fix when the selected tool failed, the user data was missing, a retrieval result was stale, or the application routed the request to the wrong agent.

Amazon Bedrock AgentCore documents tool-description recommendations specifically for ambiguous requests that cause tool-selection confusion. Its guidance is useful even if you do not use AWS: hold the current configuration and real traces together, propose a bounded description change, and test before applying it. The diagnosis still needs to establish that the wrong decision came before the downstream failure.

For each failing request, preserve the user request, available tool names and descriptions, the selected tool, its arguments, the returned result, and the next agent turn. The useful unit is a decision, not just a final answer. A final answer can look wrong because a tool was unavailable, because a tool returned bad data, or because the agent ignored a good result after calling the right tool.

Group the failures by the confusion you can name. For example, an agent may use `refund_order` whenever a user mentions a return, even when `lookup_order` should run first. Count that pattern over eligible requests and keep unknown or incomplete traces out of the denominator. A count without the available tool set does not establish why the selection happened.

Make the smallest edit that distinguishes the tools. State what the tool does, when to use it, when not to use it, required information, and the consequence of a wrong call. If two tools share a name-shaped description such as 'handle returns,' clarify the order and the precondition rather than adding a long paragraph of policy to both.

Keep the name, schema, permissions, prompt, model, routing, and retrieval configuration fixed unless one is the diagnosed cause. If you change the tool description and the system prompt together, a later result cannot tell you which change mattered. A schema change is a separate candidate, because it can alter both what the model emits and what the runtime accepts.

Run both configurations on the same requests, including the observed failure family and nearby requests that already work. Compare tool-selection accuracy, argument validity, task completion, and any cost or latency effect that matters for the workflow. The candidate should not advance because it improves one narrow example while it starts refusing legitimate tool calls elsewhere.

Arize's recent tool-description walkthrough reached the same practical conclusion: inspect the tool calls behind an answer, rerun the same tasks, and keep outcome quality in the comparison. The exact benchmark and vendor are not the point. The point is that a correct-looking final answer can hide unnecessary or incorrect tool behavior, and a lower tool-call count is not automatically an improvement.

Converra's simulation uses paired comparisons, so a challenger must beat the current configuration on conversations both versions completed. If your team changes descriptions in its own repository, use Converra to preserve the diagnosis and comparison evidence, then apply the approved configuration change through your existing deployment path. Converra does not take authority to change customer tool definitions.

Record the exact description version and release time. After the approved release, count the same wrong-tool pattern on new eligible requests with the same detector and tool inventory. Separate an approved release, a deployed configuration, and evidence that the behavior changed. A passing benchmark proves only the benchmark result.

For a low-volume agent, keep monitoring until there are enough eligible tool decisions to interpret. If another prompt, schema, model, or routing change overlaps the window, mark the outcome confounded rather than crediting the description. The guide to verifying an AI agent fix after deployment explains the comparable-window discipline.

The numbers are illustrative, not customer evidence. A support agent has `lookup_order` and `initiate_refund`. In 18 of 120 eligible refund requests, it calls `initiate_refund` before checking whether an order exists. The trace shows the same ambiguous description in every failure. The team changes only the refund tool description to require a found order ID and to direct missing IDs to lookup first.

They run 40 shared tasks. The candidate removes the early-refund call on 10 failure-family tasks, retains valid refunds on 12 known-good tasks, and has no new malformed arguments. A reviewer approves the configuration release. In the next 90 eligible production requests, the same detector finds two early-refund calls. That is evidence of improvement, not proof that the issue vanished. The team investigates the two recurrences before making a stronger claim.

If the release had also changed a prompt rule about refunds, the outcome would be confounded. If the tool had returned an order-not-found error after the correct lookup, the description was not the root cause. Those distinctions keep a polished description from becoming a story about a problem it did not solve.

A coding agent can retrieve the saved failure evidence, inspect the available tool contracts, summarize the candidate comparison, and prepare the implementation work for the team that owns the agent. Ask it to report missing traces, incomplete denominators, and overlapping changes explicitly. Do not ask it to infer success from an edited description or a green test run.

With Converra's MCP connection, begin with existing agents and run evidence, then inspect step-level diagnosis and verification evidence. Generating a candidate, running a simulation, deploying a change, and any other state-changing action remain subject to the connected account's scopes and the user's explicit authority.