Model Context Protocol (MCP) has emerged as one of the most transformative developments in AI this year. As the bridge between chat-based LLMs and fully agentic AI systems, MCP is reshaping how agents interact with APIs. The result? A wave of powerful MCP servers and a growing ecosystem of tools that make it easier than ever to convert APIs into AI-ready resources.
Whether you’re working with open-source projects or enterprise-grade platforms, there’s a tool out there to help you turn your OpenAPI specs into functional MCP servers. Below, we’ve rounded up 5 standout tools—to help you get started.
5 Tools to Build MCP Servers
1. Tyk API-to-AI
Tyk’s API-to-AI tool is more than just a converter—it’s a full-fledged API management ecosystem. It handles governance, access control, and monitoring, making it ideal for teams already using Tyk or those looking for an all-in-one solution to manage and expose APIs as MCP resources.
Tyk API-to-AI: Bridging APIs and AI with Full Lifecycle Management
Tyk API-to-AI is a developer-friendly tool that transforms your existing APIs into AI-compatible interfaces, making it easier to expose backend services to AI agents and large language models (LLMs). It’s not just a converter—it’s a full-featured API management platform that handles everything from access control to observability, all in one place.
For developers, this means:
- No need to rebuild APIs: Tyk wraps your existing endpoints and exposes them in a format that AI systems can understand and interact with.
- MCP-ready: It enables your APIs to function as Model-Compute Platform (MCP) servers, allowing AI agents to call them as tools or functions.
- Unified control plane: You get built-in governance, rate limiting, authentication (OAuth2, JWT, etc.), and analytics—without needing to stitch together multiple tools.
- Seamless integration: If you’re already using Tyk for API management, the API-to-AI feature plugs right into your current setup with minimal configuration.
Whether you’re building AI-native applications or just want to make your services accessible to LLMs, Tyk API-to-AI gives you the infrastructure to do it securely, scalably, and efficiently.
2. Speakeasy
Speakeasy offers a seamless MCP generator that transforms OpenAPI definitions into MCP servers and integrates them directly into SDKs. With type-safe validation via Zod, it’s a great pick for developers focused on security and stability.
At its core, Speakeasy automates the transformation of your OpenAPI definitions into AI-compatible endpoints. These endpoints can be directly integrated into SDKs, making it easy for developers to expose backend functionality to AI models without manual wiring or boilerplate code.
Key Features for Developers:
- OpenAPI to MCP Conversion: Speakeasy reads your OpenAPI spec and generates MCP-compliant server endpoints that AI agents can call as tools or functions.
- SDK Integration: It automatically generates SDKs in multiple languages, allowing you to consume these endpoints in your applications with minimal effort.
- Type-Safe Validation with Zod: Speakeasy uses Zod for runtime validation, ensuring that inputs and outputs are strictly typed and validated—reducing bugs and improving reliability.
- Security and Stability: With built-in validation and structured typing, Speakeasy helps enforce contract integrity between your API and AI agents, making it a solid choice for production-grade systems.
Speakeasy is ideal for developers who value automation, type safety, and clean integration between APIs and AI systems.
3. MCP.Link
Built by Automation AI Lab on Vercel, MCP.Link is a no-install, web-based tool. Just link your OpenAPI spec, customize your settings, and generate a working MCP server with a single click. It’s perfect for quick deployments. It’s designed for speed, simplicity, and zero setup—making it ideal for rapid prototyping or quick deployment of AI-accessible APIs.
How It Works:
Upload or Link Your OpenAPI Spec
You can either paste your OpenAPI JSON/YAML or link to a hosted spec (e.g., from GitHub or SwaggerHub).Customize Settings
Through a clean web interface, you can configure:- Endpoint visibility
- Tool descriptions for AI agents
- Parameter types and constraints
- Response formatting
One-Click MCP Server Generation
Once configured, MCP.Link generates a live MCP server endpoint that AI agents can call immediately. No installation, no CLI, no backend setup.
Developer Benefits:
- Zero Infrastructure Overhead: Everything runs on Vercel, so you don’t need to deploy or host anything yourself.
- Fast Iteration: Ideal for testing how your API behaves when exposed to AI agents or LLMs.
- Tool Metadata Injection: Easily annotate endpoints with descriptions and parameter schemas so AI agents can understand how to use them.
- Perfect for Hackathons, Demos, and MVPs: If you need to expose an API to AI quickly, MCP.Link is one of the fastest ways to do it.
4. Ai-Create
Developed by XXLV in Go, Ai-Create is a CLI tool that’s both simple and highly customizable. Its standout feature is the inspector, which helps debug and analyze MCP servers. It also integrates natively with Claude, making it ideal for those working in that ecosystem.
🔧 Key Developer Features
1. Simple CLI Interface
Ai-Create is designed for ease of use. You can generate an MCP server with a single command:
ai-create –oasPath ./api.yaml –version v1
This makes it ideal for developers who want to quickly test or deploy AI-accessible APIs without complex setup.
2. Highly Configurable via Flags
Despite its simplicity, Ai-Create offers a wide range of flags for customization:
--oasPath: Path to your OpenAPI spec file--version: Specify API version--port: Set the server port--toolPrefix: Prefix tool names for AI agents--output: Define output directory for generated server files--claude: Enable Claude-specific formatting and integration
This flexibility allows developers to tailor the MCP server to their specific environment and use case.
3. Inspector Module
Ai-Create’s standout feature is its Inspector, which provides powerful debugging and analysis tools:
- Endpoint Testing: Simulate tool calls with parameters
- Schema Validation: Check input/output against OpenAPI definitions
- Trace Logs: View detailed logs of AI-agent interactions
- Error Diagnosis: Identify misconfigurations or unexpected behavior
This makes Ai-Create especially valuable for developers building production-grade MCP tools or troubleshooting complex integrations.
4. Native Claude Integration
Ai-Create includes built-in support for Anthropic’s Claude, enabling:
- Direct registration of tools into Claude’s environment
- Metadata formatting optimized for Claude’s tool-calling interface
- Streamlined deployment into Claude-based workflows
This makes it a top choice for developers working with Claude or planning to integrate MCP tools into Anthropic’s ecosystem.
5. Go-Based Performance
Being written in Go, Ai-Create benefits from:
- Fast execution and low resource usage
- Easy cross-platform compilation
- Compatibility with containerized environments and CI/CD pipelines
💡 Final Thoughts
The excitement around MCP is real—and justified. But with every new innovation comes the challenge of integration. The best tools don’t just showcase new tech; they make your life easier.
When choosing an MCP generator, consider your workflow:
- CLI fans: Try
OpenAPI‑to‑MCPServer,openapi-mcp-generator,mcpgen, orAi-Create. - Python developers: Go with
FastMCPorOpenAPI-MCP. - Tyk users: Stick with
Tyk API-to-AI. - Looking for simplicity?
SpeakeasyandMCP.Linkare great standalone options.
Got a favorite tool we missed? Drop it in the comments—we’ll keep this list updated!
