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“My AI agent ignored my MCP server and grepped anyway” can describe three different failures: the server never initialized, its tools were not ready or discoverable when the turn began, or the model had the tools but chose not to call one. Check those stages in order before changing prompts. Then add a direct, task-specific instruction and verify the next run from its tool events rather than guessing from the answer.
1. Check whether the MCP server initialized
A missing tool call does not prove the model saw the server and rejected it. First determine whether the connection succeeded. OpenAI’s MCP connections guide says to inspect agent.session.turn.failed when a required server cannot initialize, and to check process logs for stdio servers. OpenAI describes the basic arrangement this way: “An MCP server publishes tool definitions and runs tool calls.”
For an HTTP server
Check that the configured server URL is reachable from the place where the connection runs. The Agents API guide distinguishes a service-origin connection from an environment-origin connection: for an environment-origin connection, confirm that the executor is attached and connected and that it can reach the server over the network. Also verify any required credentials.
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For a stdio server
Inspect the process logs and confirm that the configured command exists in the runtime, its dependencies are available, and the configured working directory is an existing absolute path. A command that works on a developer’s machine may still be unavailable in the environment where the agent runs.
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Also check whether the server is configured as required or optional. A required server that fails initialization can fail the turn; with an optional server, the turn may proceed without it. In neither case should an absent call alone be treated as evidence that the model evaluated the server’s tools.
2. Confirm the expected tool was discovered before the turn
A connected server is not enough: the particular tool must be listed and available to the turn. In OpenAI Realtime MCP, watch for mcp_list_tools.in_progress, then mcp_list_tools.completed or mcp_list_tools.failed. The completed event shows imported tool names. If a turn starts while listing is still in progress, a tool that has not loaded is not eligible for that turn. See OpenAI’s Realtime with tools guide.
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For Agents API function discovery, distinguish eager from deferred loading. OpenAI’s tool-search guide describes eager loading as a fit for a small set of functions used often, and deferred loading as a way to handle a larger catalog by discovering only a relevant subset. Deferred discovery adds a step, so confirm that the desired tool can actually be found. MCP tool search is automatic when the model and provider support it; configuring an MCP server does not by itself mean you should add a separate tool_search function.
Read the failure as a stage, not a single “ignored MCP” problem
| What you observe | Likely stage to investigate | Next check |
|---|---|---|
| Initialization failure or no usable connection | Server connection | Turn failure event, network reachability, credentials, process logs, command, dependencies, and working directory |
| Listing is in progress, failed, or omits the expected tool | Tool discovery and loading | Wait for listing completion; inspect the imported names and discovery configuration |
| Expected tool is listed before the turn, but no call is emitted | Model tool selection | Review the request, tool description, access restrictions, and tool-choice setting |
| A call is emitted but does not return a usable result | Tool execution | Inspect call arguments, result, and protocol, execution, or connectivity errors |
3. If the tool is available, diagnose selection
Once the expected tool is present before the turn, examine what the model was asked to do and what tools it was allowed to use. OpenAI’s Using tools guide explains that the model decides whether to use a configured tool based on the prompt; tool_choice can guide that decision or control it, depending on the API and setting.
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- Make the task match the tool’s purpose. If the tool searches indexed documentation, ask a question that clearly calls for that documentation rather than a task suited to local source-code search.
- Check the tool’s name and description. They should make its purpose and relevant tasks clear. A community discussion has suggested that wording can affect MCP selection, but that is anecdotal—not an official guarantee or controlled finding. Treat clearer wording as a hypothesis to test, not a proven universal cause.
- Review restrictions and choice settings. Confirm that the tool is permitted for this turn and that the configured
tool_choicematches your intended behavior. When an application must require a call, use the API’s supported controls and handle cases where no eligible tool is available.
For example, Realtime’s documentation notes that tool_choice: "required" cannot make an unavailable tool callable. If listing has not completed, wait for it or use a policy appropriate to that turn.
Make the intended tool explicit in a scoped instruction
A short instruction can make a relevant MCP tool salient without banning local search for tasks where it is appropriate. OpenAI’s Docs MCP setup guide demonstrates an AGENTS.md instruction telling an agent to use the documentation MCP for relevant OpenAI questions. That is a documented example, not a guarantee that every framework reads or honors AGENTS.md.
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Adapt the pattern to your own repository and tool names:
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Verify the change with observable events
After changing one factor, run a representative request and trace the lifecycle. OpenAI’s MCP servers documentation describes an mcp_list_tools item when discovery succeeds and an mcp_call item when a tool is selected. A failed call can include protocol, execution, or connectivity errors.
- Check the connection. Confirm initialization succeeded, or find the failure event or process log that explains why it did not.
- Check availability before the turn. Confirm listing completed and the expected tool name appears.
- Check selection. Look for an MCP call event and inspect the tool name and arguments.
- Check execution. Read the result or error fields to determine whether the server returned a useful response.
- Change one factor and repeat. Record which stage changed across runs; this separates an initialization or timing fix from a prompt or selection change.
If the call is emitted but errors, troubleshoot execution or connectivity rather than adding stronger prompt instructions. If the tool is listed and eligible but no call is emitted, focus on the request, tool description, restrictions, and tool-choice behavior. The events make those cases distinguishable; a final answer that happens to look correct does not establish which route produced it.
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