8 Comprehensive open source and hosting solutions that seamlessly convert any API to AI-Ready MCP server

Model Communications Protocol (MCP) is an emerging open standard that allows AI agents to interact with external services through a unified interface. Instead of writing custom integrations for each API, the MCP server exposes a set of tools that customer AI can dynamically discover and call. This decoupling means that API providers can develop their backend or add new operations without breaking existing AI clients. Meanwhile, AI developers get consistent protocols to call, check and combine external functions. Here are eight solutions to convert existing APIs to MCP servers. This article explains the purpose, technical approach, implementation steps or requirements of each solution, unique features, deployment strategies, and applicability to different development workflows.

Fastapi-MCP: Native Fastapi extension

FastAPI-MCP is an open source library that integrates directly with Python’s FastAPI framework. All existing rest routes will become MCP tools by instantiating a single class and installing it on your FastAPI application. The input and output patterns defined by the pydantic model are automatically performed, and the tool description comes from your routing documentation. Authentication and dependency injection behaves like normal FastAPI endpoints, ensuring that any security or verification logic you already have remains valid.

Under the hood, FastAPI-MCP hangs in the ASGI application and routes the MCP protocol to the appropriate FastApi handler inside. This avoids additional HTTP overhead and maintains high performance. The developer installs it via PIP, adding a minimum snippet, for example:

from fastapi import FastAPI
from fastapi_mcp import FastApiMCP

app = FastAPI()
mcp = FastApiMCP(app)
mcp.mount(path="/mcp")

The final MCP server can be run in the same UVICORN process or separately. Since it is fully open source for the MIT license, the team can review, extend or customize as needed.

RapidMCP: Zero Code REST to MCP Conversion Service

RapidMCP provides a managed, codeless avenue that converts existing REST APIs, especially existing REST APIs with OpenAPI specifications, into MCP servers without changing backend code. After registering an account, the developer points RapidMCP to the base URL of their API or uploads the OpenAPI document. RapidMCP then plugs the MCP server into the cloud, and the server proxy tool calls back to the original API.

Each route becomes an MCP tool, and its parameters and return types reflect the parameters and responses of the API. Since RapidMCP is in front of your service, it can provide usage analytics, real-time tracking of AI calls, and built-in rate limiting. The platform also plans self-hosting options for enterprises that require on-premises deployment. Teams that prefer the hosting experience can go from API to AI Angent compatibility in an hour at the expense of trusting third-party agents.

McPify: No-code MCP server builder with AI Assistant

McPify is a fully managed, codeless environment where users describe the features required in natural language, such as “current weather for a given city”, and the AI ​​assistant generates and hosts the corresponding MCP tools. The service hides all code generation, infrastructure provisioning and deployment details. Users interact through a chat or form interface, view the automatically generated tool description, and click Deploy.

Because McPify uses large language models to form an instant integration, it excels in rapid prototyping and enables non-developers to create accessible services. It supports common third-party APIs, provides one-click sharing of creating servers with users of other platform, and automatically processes protocol details such as streaming response and authentication. There is little direct control over code and dependencies on closed source hosting platforms.

SpeakAsy: OpenAPI-driven SDK and MCP server generator

Speakeasy is known for generating powerful end-customer SDKs from OpenAPI specifications and extends this to MCP by producing a fully-featured typescript MCP server with each SDK. After providing OpenAPI 3.X specifications for Speakeasy’s code generator, the team received:

• typing client library for calling APIs
• Documentation derived directly from specifications
• Implementation of independent MCP server in typescript

The generated server wraps each API endpoint as an MCP tool and preserves the description and model. Developers can run the server through the provided CLI or compile it into a standalone binary. Since the output is actual code, the team has full visibility, can customize behavior, add composite tools, force scope or permissions, and integrate custom middleware. This approach is ideal for organizations with mature OpenAPI workflows that want to provide AI-Ready access in a controlled, maintainable way.

Higress MCP Market: Open Source API Gateway Scale

Higress is an open source API gateway built on Envoy and Istio, extended to support MCP protocol. Its conversion tool adopts OpenAPI specifications and generates a declarative YAML configuration that the gateway uses to host the MCP server. Each API operation becomes a tool with templates and response formats for HTTP requests, all of which are defined in configuration rather than code. Higress supports the public “MCP marketplace” that publishes multiple APIs as MCP servers, enabling AI customers to centrally discover and consume them. Enterprises can self-host the same infrastructure expose hundreds of internal services through MCP. Gateway handles protocol version upgrades, limits, authentication, and observability. It is especially suitable for large-scale or multi-API environments, converting API-MCP into configuration-driven processes that seamlessly integrate with infrastructure-AS-Code pipelines.

Django-MCP: Plugin for DJANGO REST framework

Django-MCP is an open source plugin that brings MCP support to the Django REST framework (DRF). By applying Mixin to your viewset or registering an MCP router, it automatically exposes the DRF endpoint as an MCP tool. It has built-in serialization to get input patterns and uses your existing authentication backend to protect tool calls. Below, convert the MCP call to a normal DRF viewset operation, thus preserving the paging, filtering, and verification logic.

The installation requires adding the package to your requirements, including the Django-MCP application, and configuring the routing:

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 approach allows teams already invested in Django to add AI-Angent compatibility without copying the code. It also supports custom-made tool annotations via decorator for fine-tuning naming or documentation.

GraphQL-MCP: Convert GraphQl endpoint to MCP

GraphQL-MCP is a community-driven library that wraps GraphQl Server and treats its queries and mutations as a single MCP tool. It parses the GraphQL pattern to generate a tool manifest, mapping each operation to the tool name and input type. When the AI ​​proxy calls the tool, GraphQL-MCP builds and executes the corresponding GraphQL query or mutation, and then returns the result in the standardized JSON format expected by the MCP client. This solution is valuable for organizations using GraphQL, which want to leverage AI proxy without placing REST conventions or writing custom GraphQL calls. It supports features such as batch processing, authentication through existing GraphQL context mechanisms, and schema sewing to combine GraphQL services under an MCP server.

GRPC-MCP: GRPC service that bridges AI agents

GRPC-MCP focuses on revealing high-performance GRPC services to AI agents through MCP. It uses the service definition of the protocol buffer to generate a call that accepts JSON-RPC style, which the internal marshal converts to an MCP server that GRPC requests and streams the response. The developer includes a small adapter in their GRPC server code:

import "google.golang.org/grpc"
import "grpc-mcp-adapter"

func main() {
  srv := grpc.NewServer()
  myService.RegisterMyServiceServer(srv, &MyServiceImpl{})
  mcpAdapter := mcp.NewAdapter(srv)
  http.Handle("/mcp", mcpAdapter.Handler())
  log.Fatal(http.ListenAndServe(":8080", nil))
}

This makes it easy to bring low-latency, strongly typing services into the MCP ecosystem, opening the door for AI agents to directly call business-critical GRPC methods.

Select the right tool

The choice of these eight solutions depends on several factors:

• Preferred development workflow: FastAPI-MCP and DJANGO-MCP for code-first integration, SpeakAsy for specification-driven code generation, GraphQL-MCP or GRPC-MCP for non-current paradigms.
• Control and convenience: Libraries such as Fastapi-MCP, Django-MCP, and Speakeasy provide complete code control, while hosting platforms such as RapidMCP and McPify, as well as speed and easy control like McPify.
• Scale and Governance: Higress shines when converting and managing a large number of APIs in a unified gateway and has built-in routing, security, and protocol upgrades.
• Rapid Prototyping: McPify’s AI Assistant allows non-developers to rotate the MCP server immediately, ideal for experimentation and internal automation.

All of these tools are in line with evolving MCP specifications, ensuring interoperability between AI agents and services. By selecting the right converter, API providers can accelerate AI-powered workflows and authorize agents to coordinate real-world capabilities safely and effectively.