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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Not as a fixed fee just for opening an unused session. For OpenAI’s Responses API MCP integration, the documented charge is for tokens used when tool definitions are imported or tool calls are made. OpenAI says there are no additional fees per tool call. Definitions can still occupy context after import, so “not fetched again” does not mean “token-free.”
What OpenAI bills for MCP tools
OpenAI’s MCP servers guide says: “When you’re using the MCP tool, you only pay for tokens used when importing tool definitions or making tool calls. No additional fees apply per tool call.” In other words, the documented billing basis is token use—not a separate per-call fee or a stated flat charge for each MCP session.
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This is specific to OpenAI’s Responses API documentation. It should not be generalized to every MCP client, API provider, or separately billed MCP host. The guide does not describe a fixed charge simply for opening a session with an unused server.
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What happens to imported tool definitions
When you include an MCP server in the Responses API tools parameter, the API attempts to retrieve that server’s tool list. If retrieval succeeds, the response includes an mcp_list_tools item containing the imported tools.
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While that item remains in the API request’s conversation context, the API documentation says it does not fetch the list from the MCP server again at each turn. That is a statement about another remote fetch; it is not a promise that definitions retained in context consume zero tokens. OpenAI’s billing explanation ties cost to tokens used, and its tool search guide notes that unused definitions occupy context.
How to reduce unnecessary schema context
Use eager loading for a small, frequently needed set
Loading definitions up front can be straightforward when the catalog is small or its functions are relevant to most requests. The trade-off is that definitions for tools not used on a particular task can still take up context.
Consider deferred loading for large catalogs
OpenAI’s MCP guide describes deferred loading, in which individual functions are loaded when needed rather than all at once. The tool search guide presents this as a fit for large catalogs where a task is likely to use only a subset. A discovery or selection step is part of that approach, so consider whether it can find the function required for the task.
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Filter tools when the task needs only a subset
OpenAI’s Responses API MCP tool guide in the Cookbook describes using allowed_tools to reduce definition overhead, response time, and the model’s decision space. The amount of benefit depends on the server and workload; the guide’s description is not a universal token or cost estimate.
How to choose and measure
Compare approaches against real tasks rather than assuming one will always be cheaper or faster. OpenAI’s guidance identifies these useful evaluation points:
- Input-token usage: Does loading or filtering definitions reduce the schema material included for your requests?
- Task completion: Can the selected approach still find and use the function the task requires?
- Latency: Does any discovery step change end-to-end response time?
A smaller tool set may reduce definition overhead, but no guaranteed dollar saving follows without measuring token use for the workload and applying the relevant model rates. OpenAI’s guides do not provide a universal token count or percentage reduction for MCP schemas.
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