Your API is already an AI tool. Ship it as an MCP server.

Wrap the endpoints you already have in a visual workflow, hit deploy, and point Claude, Cursor or any MCP client at it. No SDK to learn, no server to run, no glue code to maintain.

Real, and yours to run

An MCP server here is just a workflow. Your workflow decides what each tool does and what it returns — so a tool can call one endpoint, chain several, reshape the result, or pause for an approval.

From API to agent tool in three steps

No new language, no infrastructure. If you can describe the call, you can ship the tool.

Writing an MCP server by hand

With SchemaBridge

Try it before you trust it

Build the server, connect your agent and watch it work before you pay a cent. The free plan is genuinely free — no card, no trial clock, no AI key to bring.

Do I have to write any code?

No. You build the workflow on a visual canvas — gateway steps for your API calls, an entry point tagged as a tool. The MCP server is generated from that workflow.

Which MCP clients can connect?

Any MCP-compatible client, including Claude Desktop and Cursor, over a local stdio connection or a secured remote URL.

Where does the server run?

SchemaBridge hosts it. There is no server for you to deploy, scale or keep alive — the workflow is the server.

What can a single tool do?

Whatever a workflow can do: call one endpoint, chain several, reshape the response, branch on a condition, or wait for a human approval before returning.

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