Proof of Concept
This application is currently a Proof of Concept (POC) under active development. Features may change, and some functionality may be incomplete. We welcome feedback and contributions as we continue to improve the platform.
How API to MCP Works
API to MCP is a platform that bridges the gap between REST APIs and the Model Context Protocol (MCP), enabling AI assistants to interact with your existing APIs seamlessly.
How It Works
Connect Your API
Start by registering your REST API endpoints. Define the base URL, request methods, and any required authentication. The platform supports various authentication methods including API keys, bearer tokens, and more.
Create an MCP Server
Create a new MCP server configuration. Each MCP server acts as a bridge between AI assistants and your APIs. You can create multiple MCP servers for different use cases or API groups.
Define MCP Tools
Map your API endpoints to MCP tools. Each tool represents a specific API operation that AI assistants can invoke. Define input schemas, descriptions, and configure how parameters are passed to your API.
Configure Field Mappings
Map fields from MCP tool inputs to your API's expected payload format. Transform data structures, rename fields, and handle different data types. The platform handles the translation automatically.
Connect AI Assistants
Once configured, your MCP server is ready to use. AI assistants can discover your tools, understand their capabilities through schemas, and invoke them seamlessly. The platform handles all the protocol communication.
Key Features
API Mapping
Map any REST API endpoint to MCP tools with flexible field transformations, parameter mapping, and custom payload schemas.
Tool Configuration
Define MCP tools with custom input schemas, descriptions, and URIs. Enable or disable tools dynamically without code changes.
Secure & Isolated
Row-level security ensures your MCPs, APIs, and tools are private. Each user only sees and manages their own configurations.
Protocol Compliant
Built on the Model Context Protocol standard, ensuring compatibility with MCP-compatible AI assistants and tools.
Use Cases
- Enable AI assistants to interact with your internal APIs and services
- Expose third-party API integrations to AI tools without writing custom code
- Create reusable API bridges for multiple AI assistant platforms
- Prototype and test API integrations with AI assistants quickly
Ready to Get Started?
Create your account and start transforming your APIs into MCP servers in minutes.