How do I deploy the Refunc MCP Server?
Apply the Kubernetes manifest from the repository and port-forward the two services (`aws-api` and `mcp-server`) to local ports 8000 and 8001 respectively.
@refunc
Basic information
Refunc Model Context Protocol Server
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryTool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
The Refunc MCP Server integrates Refunc serverless functions as tools in the Model Context Protocol (MCP), allowing you to expose functions as MCP endpoints via Server-Sent Events (SSE). It is designed for developers who want to create MCP-compatible tools from existing Refunc functions deployed on Kubernetes.
Deploy the Refunc components on Kubernetes using kubectl apply, then proxy the services locally. Install the refunc-cli and initialize a function with rfctl init. Edit the generated lambda.yaml to add an MCP event specification with a token and tool definitions. Finally, update the configuration with rfctl update-config and access the MCP SSE endpoint at the provided URL path.
refunc-cli to create and manage functions._call_method parameter.Apply the Kubernetes manifest from the repository and port-forward the two services (`aws-api` and `mcp-server`) to local ports 8000 and 8001 respectively.
The URL path format is `/namespace/<token-secret-name>/<token>/<func-name>/sse`. For the demo, it is `http://127.0.0.1:8001/refunc-system/mcp-demo/test/mcp-demo/sse`.
You need Python 3, the `refunc-cli` package installed via pip, and a Kubernetes cluster with the Refunc components deployed.
No, the data from the demo deployment is not persistent.
Define multiple entries under the `tools` array in the MCP event specification. Each tool requires a name, description, and JSON Schema for parameters. The function receives a `_call_method` parameter to identify which tool was invoked.
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