wehub-resource-sync
95 lines
3.8 KiB
Markdown
95 lines
3.8 KiB
Markdown
# Semantic Kernel as MCP Server
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This sample demonstrates how to expose your Semantic Kernel instance or a Agent as an MCP (Model Context Protocol) server.
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## Getting Started with Stdio
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To run these samples using the `stdio` transport (default), set up your MCP host (like [Claude Desktop](https://claude.ai/download) or [VSCode GitHub Copilot Agents](https://code.visualstudio.com/docs/copilot/chat/mcp-servers)) with the following configuration:
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```json
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{
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"mcpServers": {
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"sk": {
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"command": "uv",
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"args": [
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"--directory=<path to sk project>/semantic-kernel/python/samples/demos/mcp_server",
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"run",
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"sk_mcp_server.py"
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],
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"env": {
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"OPENAI_API_KEY": "<your_openai_api_key>",
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"OPENAI_CHAT_MODEL_ID": "gpt-4o-mini"
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}
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},
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"agent": {
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"command": "uv",
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"args": [
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"--directory=<path to sk project>/semantic-kernel/python/samples/demos/mcp_server",
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"run",
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"agent_mcp_server.py"
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],
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"env": {
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"AZURE_AI_AGENT_PROJECT_CONNECTION_STRING": "<your azure connection string>",
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"AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME": "<your azure model deployment name>",
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}
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}
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}
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}
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```
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Alternatively, you can run the server directly with the following command:
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```bash
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uv --directory=<path to sk project>/semantic-kernel/python/samples/demos/mcp_server run sk_mcp_server.py
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```
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or:
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```bash
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uv --directory=<path to sk project>/semantic-kernel/python/samples/demos/mcp_server run agent_mcp_server.py
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```
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## Getting Started with SSE
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To run these samples as an SSE (Server-Sent Events) server, set the same environment variables as above and run the following command:
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```bash
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uv --directory=<path to sk project>/semantic-kernel/python/samples/demos/mcp_server run sk_mcp_server.py --transport sse --port 8000
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```
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or:
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```bash
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uv --directory=<path to sk project>/semantic-kernel/python/samples/demos/mcp_server run agent_mcp_server.py --transport sse --port 8000
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```
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This will start a server that listens for incoming requests on port `8000`.
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> [!NOTE]
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> By default the SSE server binds to `127.0.0.1` (loopback) and only accepts requests
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> with a loopback `Host` header and, when present, a loopback `Origin` header. A local
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> MCP server exposes tools, plugins and model providers backed by your own credentials,
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> so it is good practice to keep it reachable only from your own machine. The
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> [MCP specification](https://modelcontextprotocol.io/) recommends validating `Origin`
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> and binding to loopback, in part to guard against [DNS rebinding](https://en.wikipedia.org/wiki/DNS_rebinding).
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>
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> You can override the bind address with `--host`, e.g. `--host 0.0.0.0` to expose the
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> server on the network. Do this only on a trusted network. The bundled Host/Origin
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> checks only allow loopback callers, so a non-loopback deployment needs proper
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> authentication - see the [`mcp_with_oauth`](../mcp_with_oauth/) sample for the
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> authenticated, Streamable-HTTP pattern recommended for production.
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---
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In both cases, `uv` will ensure that `semantic-kernel` is installed with the `mcp` extra in a temporary virtual environment.
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## Extending the sample
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The *sk_mcp_server* sample creates two functions:
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- `echo-echo_function`: A simple function that echoes back the input.
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- `prompt-prompt`: a function that uses a Semantic Kernel prompt to generate a response.
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The *agent_mcp_server* sample creates a simple agent that uses the Azure OpenAI service to generate a response.
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It exposes a single function:
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- `mcp-host`: A function that uses the Azure OpenAI service to generate a response.
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Once the server is created, you get a `mcp.server.lowlevel.Server` object, which you can then extend to add further functionality, like resources or prompts.
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