Schema import and tool generation
Import API schemas from a URL, pasted content, or uploaded files, then generate MCP tools from OpenAPI, GraphQL introspection, WSDL, or gRPC definitions.
MCP Bridge
MCP Bridge is a self-hosted platform that converts existing REST, GraphQL, SOAP, and gRPC APIs into MCP tools for AI agents. It helps teams expose and govern enterprise APIs with schema import, runtime controls, and built-in observability.
Overview
MCP Bridge is a self-hosted product that turns existing APIs into Model Context Protocol tools for AI agents and MCP-compatible clients. The homepage says it supports REST, GraphQL, SOAP, and gRPC APIs, and can generate fully typed, annotated tools from existing schemas without hand-writing tool definitions.
The product is positioned as a control layer for teams that want to expose and govern enterprise APIs for LLM use. It adds schema import, authentication handling, response post-processing, context-window management, and observability so agents can call APIs directly without building separate MCP servers for every service.
Key capabilities
Typed tools with annotations
Each operation becomes a typed MCP tool with input and output schemas, parameter mappings, behavioral annotations, and documentation.
Governance and runtime controls
Configure tool curation, response post-processing, authentication, rate limits, retries, and health checks from the control plane.
Code Mode for large APIs
Use Code Mode to replace large tool catalogs with three meta-tools, reducing context window usage when agents need to work across many operations.
Self-hosted deployment
Run the product as a self-hosted Docker container with no external SaaS dependencies at runtime, and deploy it on AWS ECS, Azure Container Apps, or another orchestrator.
AI observability
Track latency, throughput, token usage, and error rates through built-in AI observability features, with OTel listed in Enterprise.
Common use cases
Expose internal APIs to agents
Platform teams can expose internal services to AI agents through a single control plane instead of maintaining separate MCP adapters for each API.
Agent workflows with built-in controls
AI engineers can build workflows that call enterprise APIs with authentication, parameter mapping, response handling, and observability already in place.
Bridge an existing API estate
Organizations can adopt MCP as a standard interface over an existing API portfolio without refactoring backend services first.
Reduce context load for large catalogs
Teams with large APIs can use Code Mode to keep the tool surface smaller and reduce token usage during agent orchestration.
Pros and Cons
Pros
- Supports multiple API styles, including REST, GraphQL, SOAP, and gRPC.
- Generates MCP tools automatically from schemas instead of requiring manual tool definitions.
- Offers self-hosted deployment with no external SaaS dependencies at runtime.
- Includes response post-processing, authentication handling, rate limiting, retries, and observability.
- Provides Code Mode for reducing context-window usage on large APIs.
Cons
- The source does not provide a full documentation set for setup, limitations, or supported environments beyond the deployment examples on the homepage.
- Pricing details are available, but the source does not clearly describe all differences between plan feature sets in product terms.
FAQ
How does MCP Bridge connect an API to an AI agent?
MCP Bridge imports API schemas and turns operations into MCP tools. The homepage says it supports OpenAPI, GraphQL introspection, WSDL, and gRPC inputs, and that users can point the product at a schema URL, paste content, or upload files.
How is MCP Bridge deployed?
The source says it is self-hosted and can be deployed as a Docker container on AWS ECS, Azure Container Apps, or any orchestrator. The company also lists availability in Microsoft Azure Marketplace and AWS Marketplace.
Who is MCP Bridge for?
The homepage states that MCP Bridge can expose tools with authentication, parameter mappings, response post-processing, and observability. The pricing page also shows plans with collaboration and enterprise support features, but the exact feature set varies by plan.
What is Code Mode used for?
The homepage highlights Code Mode for large APIs, which replaces a full tool catalog with three meta-tools to reduce context window usage. The page also says the platform can handle tool curation, response post-processing, and AI-specific observability.
Are there any setup or environment limitations to know about?
No. The homepage says self-hosted and zero external SaaS dependencies at runtime, but the source does not provide a full list of environments, supported identity providers for all deployment modes, or detailed setup requirements.
Quick Facts
- Category
- Developer Tool
- Primary use
- Converting APIs into MCP tools for AI agents
- Deployment
- Self-hosted Docker container
- Supported API inputs
- OpenAPI, GraphQL introspection, WSDL, gRPC
- Marketplace availability
- Microsoft Azure Marketplace and AWS Marketplace
- Source domain
- mcp-bridge.ai
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