A new analysis explores the token costs associated with using MCP (Meta-Client Protocol) for tool discovery in AI agents compared to pre-defining tool manifests. The study found that the live discovery round trip is nearly free, but the definitions of chosen tools consume significant context tokens on every model call. The cost scales linearly with the number of tools and verbosity of their descriptions, with verbose descriptions costing significantly more than terse ones. For tasks involving many tools, curating a smaller, relevant subset can yield substantial savings, though MCP offers alternatives like domain splitting or on-demand definition loading. AI
IMPACT Understanding token costs for tool definitions can inform agent design and optimize LLM context window usage.
RANK_REASON Analysis of token costs for AI agent tool discovery mechanisms. [lever_c_demoted from research: ic=1 ai=1.0]
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