wehub-resource-sync
42 lines
2.2 KiB
Markdown
42 lines
2.2 KiB
Markdown
# Model Context Protocol
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The model context protocol is a standard created by Anthropic to allow models to share context with each other. See the [official documentation](https://modelcontextprotocol.io/introduction) for more information.
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It consists of clients and servers, and servers can be hosted locally, or they can be exposed as a online API.
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Our goal is that Semantic Kernel can act as both a client and a server.
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In this folder the client side of things is demonstrated. It takes the definition of a server and uses that to create a Semantic Kernel plugin, this plugin exposes the tools and prompts of the server as functions in the kernel.
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Those can then be used with function calling in a chat or agent.
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## Server types
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There are two types of servers, Stdio and Sse based. The sample shows how to use the Stdio based server, which get's run locally, in this case by using [npx](https://docs.npmjs.com/cli/v8/commands/npx).
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Some other common runners are [uvx](https://docs.astral.sh/uv/guides/tools/), for python servers and [docker](https://www.docker.com/), for containerized servers.
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The code shown works the same for a Sse server, only then a MCPSsePlugin needs to be used instead of the MCPStdioPlugin. For Streamable HTTP server, MCPStreamableHttpPlugin can be used.
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The reverse, using Semantic Kernel as a server, can be found in the [demos/mcp_server](../../demos/mcp_server/) folder.
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## Running the samples
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1. Depending on the sample you want to run:
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1. [Docker](https://www.docker.com/products/docker-desktop/) installed, for the samples that use the Github MCP server.
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1. [uv](https://docs.astral.sh/uv/getting-started/installation/) installed, for the samples that use the local MCP server.
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2. The Github MCP Server uses a Github Personal Access Token (PAT) to authenticate, see [the documentation](https://github.com/modelcontextprotocol/servers/tree/main/src/github) on how to create one.
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1. Check the comment at the start of the sample you want to run, for the appropriate environment variables to set.
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1. Install Semantic Kernel with the mcp extra:
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```bash
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pip install semantic-kernel[mcp]
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```
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4. Run any of the samples:
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```bash
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cd python/samples/concepts/mcp
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python <name>.py
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```
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