Why Your MCP Server Shouldn't Mirror Your API

MCP servers that directly mirror API endpoints can overload an agent’s context window because every tool definition, description, and parameter schema is included on each request, increasing token costs and making tool selection less reliable. Large, endpoint-based toolsets can lead to wrong-tool choices, incorrect parameters, and displacement of task-relevant context, particularly when agents connect to multiple systems; platform limits and reported engineering results are cited as evidence of this degradation. The proposed alternative is to expose a small number of task-oriented, broadly applicable capabilities and scope them to specific agent workflows, ideally keeping active tools below roughly 10–15 per agent. Auditing should identify unused tools, duplicated object-specific operations, API-oriented naming, and unnecessary data access, while use-case-specific servers can also support least-privilege governance. Connect AI presents its Universal Tools, Workspaces, and Toolkits as an implementation of this approach, providing a fixed cross-source tool set, scoped data catalogs, configurable permissions, and dedicated MCP endpoints for different agent roles.

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