FuzeMCP is a platform that turns any REST API into an MCP (Model Context Protocol) server. No SDKs required — connect your API, define your tools, and get a hosted MCP endpoint that works with Claude, Cursor, VS Code, and any MCP client.

How do I turn my API into an MCP server?

Sign up for free, create a project pointing to your API base URL, define your tools by mapping API endpoints to MCP tool definitions with JSON schemas, and get a hosted MCP endpoint. Connect it to any MCP-compatible client in minutes.

Do I need to know the MCP protocol?

No. FuzeMCP handles all the MCP protocol details for you, including JSON-RPC handling, transport, and server management. You just need to define your API endpoints and their schemas.

Which AI tools work with FuzeMCP?

FuzeMCP works with any client that supports the Model Context Protocol, including Claude Desktop, Cursor, VS Code, Windsurf, and OpenCode.

MCP Resources Explained: Beyond Tools and Function Calling

Tools vs Resources: Two Halves of MCP

Most MCP discussions focus on tools — functions that AI models can call to perform actions. But MCP has a second primitive that is equally important: resources.

Tools perform actions (fetch data, create records, send emails)
Resources provide context (documentation, schemas, reference data)

Think of tools as verbs and resources as nouns. An AI model needs both to be effective.

What Are MCP Resources?

A resource in MCP is a piece of data that an AI client can read to gain context. Resources are exposed by MCP servers and can be:

API documentation and OpenAPI specs
Database schemas
Configuration files
Company policies
Reference data (country lists, tax rates, etc.)
Prompt templates

Resources have a URI that the client uses to fetch them. They are read-only — unlike tools, resources do not perform side effects.

resource://schema/products
resource://docs/api-reference
resource://config/rate-limits

Why Resources Matter

Without resources, an AI model is guessing. It knows it can call get_product, but it does not know:

What parameters are valid
What the response looks like
What edge cases exist
When to use this tool vs another

Resources fill that gap. They give the model the same context a human developer gets from reading documentation.

Resources in Practice

With a FuzeMCP project, you can define resources alongside your tools. For example:

Expose your OpenAPI spec as a resource so the AI knows your full API surface
Expose your database schema so the AI writes correct queries
Expose your style guide so the AI generates consistent code

The AI client fetches these resources at startup and uses them to make better decisions.

Combining Tools and Resources

The real power comes from combining both:

AI fetches resource://schema/products — learns the data model
AI calls search_products tool — queries with correct parameters
AI fetches resource://docs/templates — gets the output format
AI presents results in the expected format

This is how professional MCP integrations work.

Resources are what make AI integrations reliable instead of hit-or-miss. FuzeMCP lets you define both tools and resources — no protocol code required.