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Reducing LLM API Costs

LLM inference engines—whether accessed via Anthropic, OpenAI, or Google—base their billing architecture strictly on Token Usage (measured in $X per 1M Input Tokens). Standard REST API responses natively return immense amounts of “noise” implicitly useful to frontend developers but completely irrelevant to an autonomous AI agent. Examples include:
  • Pagination cursors (next_url, has_more)
  • Internal routing UUIDs and hypermedia links (_links, self)
  • Null variables resulting from incomplete external forms
  • Styling or UI rendering flags (is_hidden, color_hex)
By utilizing HasMCP’s JMESPath Pruning or Goja JS Interceptors on your Provider Tools, you structurally drop these useless structural arrays natively at the proxy level. The result: HasMCP transforms a standard 50,000-token raw dump into a surgically precise 1,500-token semantic object. You directly save an average of 92% on incoming orchestration token costs inherently, predictably multiplying savings across millions of agent interactions natively.