What is MCP Bridge by Appfactor?

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.

Who it's for

• Developers building AI integrations who need to connect legacy or modern APIs to LLM agents without writing and maintaining hundreds of individual MCP servers.
• Platform engineering teams that want a single point of control to expose, govern, and optimize API resources for AI consumption across their organization.
• API owners migrating to AI-native workflows who need to add semantic context, tool boundaries, and token-efficient responses to APIs that weren't designed for LLM consumers.

Key features

Schema-driven auto-generation

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.

Self-hosted and data-private

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.

Code Mode for large APIs

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.

AI-specific observability

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.

What stands out

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.

Worth checking out if…

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.