MCP Token Costs: You Pay for the Tools Your Agent Never Calls

MCP tool definitions add recurring input-token costs because names, descriptions, schemas, and returned results are included in model context on repeated calls, while large or overlapping catalogs can also reduce reliability by making tool selection and parameter use more difficult. The article distinguishes definition, execution, and reasoning overhead, arguing that larger context windows increase capacity but do not eliminate processing or billing costs. It recommends designing tools around user outcomes rather than low-level endpoints, writing clear schemas, limiting tool-result size, loading tools only when relevant, and using programmatic execution for complex multi-step workflows. Reported experiments indicate that these approaches can substantially reduce tokens and tool calls while improving accuracy, although results depend on the model, client, workload, and tool design. Prompt caching complicates tool filtering because changing definitions or their order can invalidate reusable prompt prefixes, so stable catalogs combined with provider-supported access restrictions or deferred discovery may be more efficient across repeated requests. Eden AI is presented as an example of a hosted MCP capability layer that consolidates expert-model functions such as OCR, search, translation, and media processing, while developers retain responsibility for selecting tools, controlling returned context, measuring caching and token usage, and optimizing total successful-task cost rather than prompt size alone.

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