Point MCP Bridge at any REST, GraphQL, SOAP, or gRPC API. It auto-generates MCP tool definitions with typed schemas, auth, rate limiting, and response processing. Your LLM agents call enterprise APIs through one standard interface.
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MCP Bridge is a self-hosted tool that automatically generates Model Context Protocol (MCP) tool definitions from any existing API — REST, GraphQL, SOAP, or gRPC. Instead of writing glue code or manually defining tools for each LLM agent, you point MCP Bridge at a schema URL and it produces fully typed, annotated MCP tools ready for Claude, GPT, Gemini, or any MCP-compatible client. It handles authentication, rate limiting, and response processing, giving you a single standard interface for all your enterprise APIs.
MCP Bridge parses OpenAPI 3, GraphQL introspection, WSDL, and .proto files automatically. Every operation from your schema becomes a fully described MCP tool with typed input/output schemas, parameter mappings, and behavioral annotations — no manual tool definitions required.
The tool runs as a Docker container on AWS ECS, Azure Container Apps, or any orchestrator. Your data never leaves your network, and there are zero external SaaS dependencies at runtime. Built in Rust for memory safety and high throughput.
For APIs with hundreds of endpoints, Code Mode replaces the full tool catalog with just 3 meta-tools. This cuts context window usage by approximately 98% — from roughly 48,000 tokens down to 960 — while the LLM orchestrates calls through a secure Boa sandbox.
MCP Bridge provides observability across latency, throughput, token usage, and error rates, with OpenTelemetry support. Unlike traditional API gateways, it tracks metrics that matter for AI agents, including token waste and tool selection accuracy.
MCP Bridge translates APIs into semantically rich tool definitions LLMs can reason about, not just HTTP requests to route.
Traditional API gateways handle HTTP traffic but add no semantic context for AI agents. MCP Bridge goes further by annotating tools as read-only, idempotent, or destructive, adding documentation, and post-processing responses to reduce token waste. It handles tool curation, context window management, and AI-specific observability — things gateways were never designed for.
You're building AI agent integrations and want to connect multiple enterprise APIs through one standard interface without writing glue code. It's especially useful if you're working with legacy APIs that lack semantic context, or if you need to keep data self-hosted and under your control.
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