Overview / Description
MCP Bridge by Appfactor is an AI developer tool that automatically turns any existing API into Model Context Protocol (MCP) tools that LLM agents can call. You point MCP Bridge at any REST, GraphQL, SOAP, or gRPC schema (OpenAPI 3, GraphQL introspection, WSDL, or .proto files), and it parses every operation into a fully typed, annotated MCP tool with auth, rate limiting, and response processing, ready for Claude, GPT, Gemini, Mistral, or any MCP-compatible client. It is self-hosted as a Docker container that can run on AWS ECS, Azure Container Apps, or any orchestrator, so your data never leaves your network, and it is built in Rust for memory safety and high throughput with zero external SaaS dependencies at runtime. Rather than hand-writing tool definitions or maintaining hundreds of individual MCP servers, teams expose, govern, and optimize their APIs through a single point of control. A "Code Mode" feature is designed to cut token usage dramatically (the site cites 960 tokens versus roughly 48,000 raw) so legacy APIs stop burning through model context windows. It is aimed at developers and enterprises making existing APIs AI-ready.
Used For
Developers and enterprises use MCP Bridge by Appfactor to auto-generate typed MCP tools from existing REST, GraphQL, SOAP, and gRPC APIs for LLM agents.
Pricing
Pros & Cons
Pros
- Auto-generates typed MCP tools from REST, GraphQL, SOAP, and gRPC schemas
- Self-hosted Docker container so data never leaves your network
- Built in Rust with no external SaaS dependencies at runtime
- Code Mode cuts context tokens dramatically (cited 960 vs ~48,000 raw)
- Works with Claude, GPT, Gemini, Mistral, or any MCP-compatible client
Cons
- Requires self-hosting and Docker/orchestration knowledge to deploy
- Aimed at enterprise and developer users, not non-technical teams
- Value depends on having existing APIs with usable schemas
- Newer product in the emerging MCP tooling space