The Mannequin Communication Protocol (MCP) is an rising open normal that enables AI brokers to work together with exterior providers via a uniform interface. As a substitute of writing customized integrations for every API, an MCP server exposes a set of instruments {that a} consumer AI can uncover and invoke dynamically. This decoupling means API suppliers can evolve their again ends or add new operations with out breaking current AI shoppers. On the identical time, AI builders acquire a constant protocol to name, examine, and mix exterior capabilities. Beneath are eight options for changing current APIs into MCP servers. This text explains every answer’s function, technical method, implementation steps or necessities, distinctive options, deployment methods, and suitability for various growth workflows.
FastAPI-MCP: Native FastAPI Extension
FastAPI-MCP is an open-source library that integrates instantly with Python’s FastAPI framework. All current REST routes develop into MCP instruments by instantiating a single class and mounting it in your FastAPI app. Enter and output schemas outlined through Pydantic fashions carry over robotically, and the software descriptions derive out of your route documentation. Authentication and dependency injection behave precisely as in regular FastAPI endpoints, guaranteeing that any safety or validation logic you have already got stays efficient.
Below the hood, FastAPI-MCP hooks into the ASGI software and routes MCP protocol calls to the suitable FastAPI handlers in-process. This avoids additional HTTP overhead and retains efficiency excessive. Builders set up it through pip, add a minimal snippet corresponding to:
from fastapi import FastAPI
from fastapi_mcp import FastApiMCP
app = FastAPI()
mcp = FastApiMCP(app)
mcp.mount(path="/mcp")The ensuing MCP server can run on the identical Uvicorn course of or individually. As a result of it’s absolutely open-source underneath the MIT license, groups can audit, lengthen, or customise it as wanted.
RapidMCP: Zero-Code REST-to-MCP Conversion Service
RapidMCP offers a hosted, no-code pathway to rework current REST APIs, notably these with OpenAPI specs, into MCP servers with out altering backend code. After registering an account, a developer factors RapidMCP at their API’s base URL or uploads an OpenAPI doc. RapidMCP then spins up an MCP server within the cloud that proxies software calls again to the unique API.
Every route turns into an MCP software whose arguments and return sorts mirror the API’s parameters and responses. As a result of RapidMCP sits in entrance of your service, it will probably provide utilization analytics, reside tracing of AI calls, and built-in fee limiting. The platform additionally plans self-hosting choices for enterprises that require on-premises deployments. Groups preferring a managed expertise can go from API to AI-agent compatibility in underneath an hour, on the expense of trusting a third-party proxy.
MCPify: No-Code MCP Server Builder with AI Assistant
MCPify is a totally managed, no-code setting the place customers describe desired performance in pure language, corresponding to “fetch present climate for a given metropolis”, and an AI assistant generates and hosts the corresponding MCP instruments. The service hides all code technology, infrastructure provisioning, and deployment particulars. Customers work together through a chat or kind interface, overview robotically generated software descriptions, and deploy with a click on.
As a result of MCPify leverages giant language fashions to assemble integrations on the fly, it excels at speedy prototyping and empowers non-developers to craft AI-accessible providers. It helps frequent third-party APIs, affords one-click sharing of created servers with different platform customers, and robotically handles protocol particulars corresponding to streaming responses and authentication. The trade-off is much less direct management over the code and reliance on a closed-source hosted platform.
Speakeasy: OpenAPI-Pushed SDK and MCP Server Generator
Speakeasy is understood for producing strongly typed consumer SDKs from OpenAPI specs, and it extends this functionality to MCP by producing a totally practical TypeScript MCP server alongside every SDK. After supplying an OpenAPI 3.x spec to Speakeasy’s code generator, groups obtain:
- A typed consumer library for calling the API
- Documentation derived instantly from the spec
- A standalone MCP server implementation in TypeScript
The generated server wraps every API endpoint as an MCP software, preserving descriptions and fashions. Builders can run the server through a supplied CLI or compile it to a standalone binary. As a result of the output is precise code, groups have full visibility and may customise conduct, add composite instruments, implement scopes or permissions, and combine customized middleware. This method is right for organizations with mature OpenAPI workflows that wish to supply AI-ready entry in a managed, maintainable manner.
Higress MCP Market: Open-Supply API Gateway at Scale
Higress is an open-source API gateway constructed atop Envoy and Istio, prolonged to help the MCP protocol. Its conversion software takes an OpenAPI spec and generates a declarative YAML configuration that the gateway makes use of to host an MCP server. Every API operation turns into a software with templates for HTTP requests and response formatting, all outlined in configuration relatively than code. Higress powers a public “MCP Market” the place a number of APIs are printed as MCP servers, enabling AI shoppers to find and devour them centrally. Enterprises can self-host the identical infrastructure to reveal a whole lot of inside providers through MCP. The gateway handles protocol model upgrades, fee limiting, authentication, and observability. It’s notably effectively fitted to large-scale or multi-API environments, turning API-MCP conversions right into a configuration-driven course of that integrates seamlessly with infrastructure-as-code pipelines.
Django-MCP: Plugin for Django REST Framework
Django-MCP is an open-source plugin that brings MCP help to the Django REST Framework (DRF). By making use of a mixin to your view units or registering an MCP router, it robotically exposes DRF endpoints as MCP instruments. It introspects serializers to derive enter schemas and makes use of your current authentication backends to safe software invocations. Beneath, MCP calls are translated into regular DRF viewset actions, preserving pagination, filtering, and validation logic.
Set up requires including the bundle to your necessities, together with the Django-MCP software, and configuring a route:
from django.urls import path
from django_mcp.router import MCPRouter
router = MCPRouter()
router.register_viewset('mcp', MyModelViewSet)
urlpatterns = [
path('api/', include(router.urls)),
]This method permits groups already invested in Django so as to add AI-agent compatibility with out duplicating code. It additionally helps customized software annotations through decorators for fine-tuned naming or documentation.
GraphQL-MCP: Changing GraphQL Endpoints to MCP
GraphQL-MCP is a community-driven library that wraps a GraphQL server and exposes its queries and mutations as particular person MCP instruments. It parses the GraphQL schema to generate software manifests, mapping every operation to a software identify and enter kind. When an AI agent invokes a software, GraphQL-MCP constructs and executes the corresponding GraphQL question or mutation, then returns the leads to a standardized JSON format anticipated by MCP shoppers. This answer is efficacious for organizations utilizing GraphQL who wish to leverage AI brokers with out deciding on a REST conference or writing bespoke GraphQL calls. It helps options like batching, authentication through current GraphQL context mechanisms, and schema stitching to mix GraphQL providers underneath one MCP server.
gRPC-MCP: Bridging gRPC Providers for AI Brokers
gRPC-MCP focuses on exposing high-performance gRPC providers to AI brokers via MCP. It makes use of protocol buffers’ service definitions to generate an MCP server that accepts JSON-RPC-style calls, internally marshals them to gRPC requests, and streams responses. Builders embody a small adapter of their gRPC server code:
import "google.golang.org/grpc"
import "grpc-mcp-adapter"
func essential() {
srv := grpc.NewServer()
myService.RegisterMyServiceServer(srv, &MyServiceImpl{})
mcpAdapter := mcp.NewAdapter(srv)
http.Deal with("/mcp", mcpAdapter.Handler())
log.Deadly(http.ListenAndServe(":8080", nil))
}This makes it simple to deliver low-latency, strongly typed providers into the MCP ecosystem, opening the door for AI brokers to name business-critical gRPC strategies instantly.
Selecting the Proper Instrument
Deciding on amongst these eight options depends upon a number of elements:
- Most popular growth workflow: FastAPI-MCP and Django-MCP for code-first integration, Speakeasy for spec-driven code technology, GraphQL-MCP or gRPC-MCP for non-REST paradigms.
- Management versus comfort: Libraries like FastAPI-MCP, Django-MCP, and Speakeasy give full code management, whereas hosted platforms like RapidMCP and MCPify commerce off some management for velocity and ease.
- Scale and governance: Higress shines when changing and managing giant numbers of APIs in a unified gateway, with built-in routing, safety, and protocol upgrades.
- Fast prototyping: MCPify’s AI assistant permits non-developers to spin up MCP servers immediately, which is right for experimentation and inside automation.
All these instruments adhere to the evolving MCP specification, guaranteeing interoperability amongst AI brokers and providers. By selecting the best converter, API suppliers can speed up the adoption of AI-driven workflows and empower brokers to orchestrate real-world capabilities safely and effectively.
Sana Hassan, a consulting intern at Marktechpost and dual-degree scholar at IIT Madras, is captivated with making use of expertise and AI to handle real-world challenges. With a eager curiosity in fixing sensible issues, he brings a contemporary perspective to the intersection of AI and real-life options.
