The model communication protocol (MCP) is an emerging open standard which allows AI agents to interact with external services via a uniform interface. Instead of writing personalized integrations for each API, an MCP server exhibits a set of tools that a IA customer can discover and invoke dynamically. This decoupling means that API suppliers can change their back-end or add new operations without breaking existing AI customers. At the same time, AI developers get a coherent protocol to call, inspect and combine external capacities. You will find below eight solutions to convert the existing APIs to MCP servers. This article explains the objective of each solution, the technical approach, the stages or the implementation requirements, the unique features, the deployment strategies and the adequacy for various development flows of development.
Fastapi-McP: Native Fastapi Extension
Fastapi-mcp is an open source library that integrates directly into the Fastapi framework in Python. All existing rest roads become MCP tools by instantling a single class and by going up on your Fastapi application. The input and output schemes defined via pydantic models reproduce automatically, and tool descriptions derive from your road documentation. Authentication and dependence injection behaves exactly as in normal Fastapi termination criteria, ensuring that any safety or validation logic that you have already remains effective.
Under the hood, Fastapi-MCP clings to the ASGI application and transports the MCP protocol to calls for the processing of appropriate Fastapi managers. This avoids additional HTTP costs and maintains high performance. The developers install it via PIP, add a minimum extract such as:
from fastapi import FastAPI
from fastapi_mcp import FastApiMCP
app = FastAPI()
mcp = FastApiMCP(app)
mcp.mount(path="/mcp")The resulting MCP server can work on the same uvicorne process or separately. Since it is completely open-source under the MIT license, the teams can audit, extend it or customize it if necessary.
RAPIDMCP: Rest conversion service to MCP zero code
Rapidmcp Provides a hosted and without code route to transform existing REST APIs, in particular those with OPENAPI specifications, into MCP servers without modifying the Backend code. After saving an account, a developer Pointe RapidMCP on the basic URL of his API or download an OPENAPI document. RAPIDMCP then runs an MCP server in the Cloud which proxies the tool reminds the original API.
Each route becomes an MCP tool whose arguments and types of return reflect the parameters and responses of the API. Since RAPIDMCP is in front of your service, it can provide use analyzes, live tracing from AI calls and integrated rate limitation. The platform also provides self-hosting options for companies that require on-site deployments. Teams that prefer a managed experience can go from API to a-annoying compatibility in less than an hour, to the detriment of the confidence of a third-party proxy.
McPIFY: No code MCP Server Builder with Assistant AI
Mcpify is an entirely managed and code -free environment where users describe the desired features in natural language, such as “responding to the current weather for a given city”, and an AI assistant generates and hosts the corresponding MCP tools. The service masks all code creations, infrastructure supply and deployment details. Users interact via a cat or form interface, automatically examine the tool descriptions generated automatically and deploy by clicking.
Since MCPIFY uses models of large languages to assemble integrations on the fly, it excels in rapid prototyping and empowered non-developmenters to create services accessible to AI. It supports common third-party APIs, offers sharing with one click of servers created with other users of the platform and automatically manages the details of the protocol such as streaming and authentication responses. Compromise is a less direct control over the code and dependence on a closed source platform.
Speakeasy: SDK and MCP server generator focused on Openapi
Speakeasy is known to generate highly typed customer SDKs from OPENAPI specifications, and it extends this capacity to MCP by producing an fully functional MCP server alongside each SDK. After providing an OPENAPI 3.X specification to the speakeasy code generator, the teams receive:
- A Tyrée Customer Library to call the API
- Documentation derived directly from the specification
- Autonomous MCP server implementation in Typecript
The server generated envelops each point of termination of the API as a MCP tool, preserving descriptions and models. Developers can execute the server via an provided clip or compile it to an autonomous binary. Since the output is of the real code, the teams have complete visibility and can personalize behavior, add composite tools, apply glasses or authorizations and integrate personalized middleware. This approach is ideal for organizations with mature Openapi workflows that wish to offer ready -made access for AI in a controlled and maintained manner.
Higress MCP Marketplace: Open Source Api Passerelle on a large scale
Higress is an open source api bridge built at the top of the envoy and Istio, extended to take care of the MCP protocol. His conversion tool takes an OPENAPI specification and generates a declarative YAML configuration that the gateway uses to host an MCP server. Each API operation becomes a tool with models for HTTP requests and response formatting, all defined in configuration rather than code. Higress feeds a public “MCP market” where several APIs are published in the form of MCP servers, allowing AI customers to discover them and consume them centrally. Companies can self-heber the same infrastructure to exhibit hundreds of internal services via MCP. The bridge manages upgrades to the version of the protocol, limitation of rates, authentication and observability. It is particularly well suited to large-scale or multi-depth environments, transforming API-MCP conversions into a configuration process that is transparently integrated into infrastructure pipelines as a code.
Django-mcp: plugin for django rest framework
Django-mcp is an open source plugin that provides MCP support to the Django Rest (DRF) framework. By applying a mixt to your views or by recording an MCP router, it automatically exposes the DRF termination points as MCP tools. He introspects for serializers to derive from the input patterns and uses your existing authentication backends to secure the invocations of tools. Below, MCP calls are translated into normal DRF views, preserving pagination, filtering and validation logic.
Installation requires adding the package to your requirements, including the Django-MCP application 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 approach allows the teams already invested in Django to add an AI-AGENT compatibility without duplication of code. It also supports personalized tool annotations via decorators for refined denomination or documentation.
Graphql-mcp: Convert the Graphql termination points to MCP
Graphql-mcp is a library focused on the community that envelops a graphql server and exposes its requests and mutations as individual MCP tools. He analyzes the graphql diagram to generate manifestos of tools, mapping each operation with a tool name and a type of input. When an AI agent invokes a tool, graphql-mcp built and executes the corresponding graphql request or mutation, then returns the results in a standardized JSON format expected by MCP customers. This solution is valuable for organizations using Graphql who wish to take advantage of AI agents without registering on a rest agreement or writing tailor -made graphql calls. It supports features such as batch, authentication via existing graphql context mechanisms and diagram seams to combine graphql services under a single MCP server.
GRPC-MCP: Bridging GRPC Services for AI agents
GRPC-MCP focuses on exposure of high performance GRPC services to AI agents via MCP. It uses the definitions of service of the protocol stamps to generate an MCP server which accepts JSON-RPC style calls, them brings together internally to GRPC requests and diffuses responses. The developers include 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 facilitates the introduction of low latency and highly typed services in the MCP ecosystem, directly opening the door to AI agents to directly call critical GRPC methods.
Choose the right tool
The selection between these eight solutions depends on several factors:
- Favorite development work thread: Fastapi-MCP and DJANGO-MCP for the integration of FIRST code, speakeeasy for code generation, graphql-mcp or GRPC-MCP for non-relative paradigms.
- Control of the lack: libraries like Fastapi-MCP, Django-MCP and Speakeasy give complete code control, while the platforms hosted like RapidMCP and MCPIFY compromise a certain control for speed and ease.
- Scale and governance: Higress shines when converting and managing a large number of APIs in a unified gateway, with integrated routing, safety and protocol improvements.
- Rapid prototyping: MCPIFY AI assistant allows non-developmenters to instantly turn MCP servers, which is ideal for experimentation and internal automation.
All these tools adhere to the evolution of the MCP specification, guaranteeing interoperability between AI agents and services. By choosing the right converter, API suppliers can accelerate the adoption of AI -focused workflows and allow agents to orchestrate real capacities in complete safety and effectively.
Sana Hassan, consulting trainee at Marktechpost and double -degree student at Iit Madras, is passionate about the application of technology and AI to meet the challenges of the real world. With a great interest in solving practical problems, it brings a new perspective to the intersection of AI and real life solutions.
