The Mannequin Communication Protocol (MCP) is an rising open customary that permits AI brokers to work together with exterior providers by means of a uniform interface. As a substitute of writing customized integrations for every API, an MCP server exposes a set of instruments {that a} shopper AI can uncover and invoke dynamically. This decoupling means API suppliers can evolve their again ends or add new operations with out breaking present AI shoppers. On the similar time, AI builders acquire a constant protocol to name, examine, and mix exterior capabilities. Under are eight options for changing present 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 straight with Python’s FastAPI framework. All present REST routes grow to be MCP instruments by instantiating a single class and mounting it in your FastAPI app. Enter and output schemas outlined by way of Pydantic fashions carry over routinely, 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 utility and routes MCP protocol calls to the suitable FastAPI handlers in-process. This avoids further HTTP overhead and retains efficiency excessive. Builders set up it by way of pip, add a minimal snippet similar 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 beneath 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 remodel present REST APIs, significantly 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 might provide utilization analytics, reside tracing of AI calls, and built-in charge 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 beneath 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, similar 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 by way of a chat or kind interface, evaluate routinely 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 fast prototyping and empowers non-developers to craft AI-accessible providers. It helps widespread third-party APIs, gives one-click sharing of created servers with different platform customers, and routinely handles protocol particulars similar 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 shopper 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 shopper library for calling the API
- Documentation derived straight 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 by way of a supplied CLI or compile it to a standalone binary. As a result of the output is precise code, groups have full visibility and might customise habits, 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 method.
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 quite than code. Higress powers a public “MCP Market” the place a number of APIs are revealed as MCP servers, enabling AI shoppers to find and eat them centrally. Enterprises can self-host the identical infrastructure to reveal a whole lot of inside providers by way of MCP. The gateway handles protocol model upgrades, charge limiting, authentication, and observability. It’s significantly nicely suited 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 routinely exposes DRF endpoints as MCP instruments. It introspects serializers to derive enter schemas and makes use of your present 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 package deal to your necessities, together with the Django-MCP utility, 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 by way of 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 sort. 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 selecting a REST conference or writing bespoke GraphQL calls. It helps options like batching, authentication by way of present GraphQL context mechanisms, and schema stitching to mix GraphQL providers beneath one MCP server.
gRPC-MCP: Bridging gRPC Companies for AI Brokers
gRPC-MCP focuses on exposing high-performance gRPC providers to AI brokers by means of 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 embrace a small adapter of their gRPC server code:
import "google.golang.org/grpc"
import "grpc-mcp-adapter"
func predominant() {
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 straightforward to deliver low-latency, strongly typed providers into the MCP ecosystem, opening the door for AI brokers to name business-critical gRPC strategies straight.
Selecting the Proper Software
Deciding on amongst these eight options is determined by a number of components:
- 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.
- Speedy 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 pupil at IIT Madras, is obsessed with making use of know-how 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.