MCP Bridge by Appfactor
UnclaimedConnect any API to any AI agent by auto-generating Model Context Protocol tool definitions.
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Tracked since2026
0 reviews trackedThe Bottom Line
Entry price
Free plan available, paid tiers above
Biggest pro
Significantly reduces development time for integrating APIs with AI agents.
Biggest con
Requires self-hosting and Docker knowledge for deployment.
TL;DR - MCP Bridge by Appfactor
- Automatically generates AI-ready tool definitions from existing APIs (REST, GraphQL, SOAP, gRPC).
- Self-hosted solution that optimizes API interactions for LLMs, reducing context window usage.
- Provides a unified control plane for API exposure, governance, and AI-specific observability.
Pricing: Free plan available
Best for: Growing teams
What is MCP Bridge by Appfactor?
MCP Bridge by Appfactor is a self-hosted solution designed to bridge the gap between existing APIs and AI agents, particularly Large Language Models (LLMs). It automatically generates Model Context Protocol (MCP) tool definitions from various API specifications like REST, GraphQL, SOAP, and gRPC. This eliminates the need for manual tool definition, enabling developers to quickly expose their APIs to AI agents like OpenAI, Claude, Mistral, and Gemini without extensive rewrites or 'glue code'.
The product addresses the challenge that traditional APIs were not built with LLM consumption in mind, often lacking semantic context and efficient payload structures for AI. MCP Bridge provides a single control point to expose, govern, and optimize LLM, MCP, and API resources. It offers features like schema-driven tool generation, runtime execution with input validation and authentication, response post-processing to reduce token waste, and a unique "Code Mode" for large APIs to significantly cut down context window usage. Built in Rust, it is memory-safe, high-throughput, and production-ready, with no external SaaS dependencies at runtime, ensuring data remains within the user's network.
Pros & Cons
Pros
- Significantly reduces development time for integrating APIs with AI agents.
- Optimizes token usage for LLMs, leading to cost savings and improved performance.
- Self-hosted architecture ensures data privacy and security.
- Supports a wide range of API protocols (REST, GraphQL, SOAP, gRPC).
- Provides AI-specific observability and governance features.
Cons
- Requires self-hosting and Docker knowledge for deployment.
- May have a learning curve for understanding MCP and AI agent integration concepts.
- Specific details on advanced features like custom JavaScript sandboxing might require deeper technical understanding.
Key Features
Auto-generate MCP tool definitions from API schemas (OpenAPI, GraphQL, WSDL, gRPC)Self-hosted deployment via Docker on various orchestrators (AWS ECS, Azure Container Apps)Runtime execution with input validation, parameter mapping, and authentication handlingResponse post-processing to reduce token waste for LLMsCode Mode for large APIs to reduce context window usage by ~98% with meta-toolsTool curation (enable, rename, edit descriptions, customize parameter mappings)Enterprise authentication support (Bearer, Basic, API Key, OAuth2, AWS Cognito SRP, OIDC)Analytics dashboard for latency, throughput, token usage, and error rates
Pricing Plans
Free TrialStarter
$0,00 / month
- One user
- Basic AI Assistant
- Limited Brand Voice
- Email support
- Basic analytics
Team
$449 / month
- Everything in Creator, plus:
- 3 user seats, expandable up to 5
- 3 Custom Brand Voices
- Content performance insights
- Team collaboration tools
Enterprise
$999 / month
- Everything in Pro, plus:
- Unlimited user seats
- Dedicated account manager
- Advanced analytics dashboard
- Customizable workflows
Free
$0,00 / year
- 1 user seat
- Basic AI assistance
- Limited Brand Voice
- Email support
- Basic analytics
Professional
$4499 / year
- Everything in Creator, plus:
- 3 user seats, expandable up to 5
- 3 Custom Brand Voices
- Content performance insights
- Team collaboration tools
Enterprise
$9999 / year
- Everything in Pro, plus:
- Unlimited user seats
- Dedicated account manager
- Advanced analytics dashboard
- Customizable workflows
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MCP Bridge by Appfactor FAQ
How does MCP Bridge handle authentication for the backend APIs it exposes to AI agents?
MCP Bridge supports various enterprise authentication methods including Bearer tokens, Basic authentication, API Keys, OAuth2, and AWS Cognito SRP. For the web UI, it also integrates with OIDC providers like Entra ID, Keycloak, Auth0, and Okta, ensuring secure access and operation.
What is 'Code Mode' and how does it reduce context window usage for large APIs?
Code Mode is a feature designed for large APIs where hundreds of individual tool definitions would consume significant LLM context. Instead, it replaces the full catalog with three meta-tools. The LLM then orchestrates calls by discovering tools on demand and executing JavaScript within a secure Boa sandbox, cutting context window usage by approximately 98% (e.g., from ~48,000 tokens to ~960 tokens).
Can MCP Bridge be deployed in a serverless environment, and what are its runtime dependencies?
MCP Bridge is designed for self-hosting in Docker containers, making it suitable for deployment on orchestrators like AWS ECS or Azure Container Apps. It is built in Rust, ensuring memory safety and high-throughput, and has zero external SaaS dependencies at runtime, meaning all operations occur within your network.
How does MCP Bridge differentiate itself from traditional API gateways when integrating with AI agents?
Unlike traditional API gateways that primarily route HTTP requests, MCP Bridge is specifically built for AI agents. It translates APIs into semantically rich tool definitions that LLMs can reason about, select, and call efficiently. It handles AI-specific concerns such as tool curation, response post-processing to reduce token waste, context window management, and AI-specific observability (latency, throughput, token usage, error rates), which gateways were not designed for.
Source: mcp-bridge.ai