An open-source, vendor-agnostic MCP governance proxy that sits between AI clients and tool servers to apply response projection, metering, and retry policies, reducing token bloat and providing per-tool cost visibility.
MCP server that scores tool descriptions, estimates token costs, simulates agent tool selection, and generates reliability reports to help AI agents choose the right tools and reduce wasted tokens.
Optimizes MCP tool menus from OpenAPI specs by grouping operations by tag and trimming schemas to reduce token usage, improving agent tool-selection accuracy.
A proxy server that wraps existing MCP servers to significantly reduce token consumption by compressing tool descriptions into a two-step interface. It enables users to integrate extensive toolsets without exceeding context limits or incurring high API costs.