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Python: Clarify remote MCP setup in sample guide #14379

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Python: Clarify remote MCP setup in sample guide
georgeatparallel Sep 5, 2026
89448fd
Python: Clarify MCP plugin naming and SSE terminology
georgeatparallel Sep 5, 2026
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28 changes: 26 additions & 2 deletions python/samples/concepts/mcp/README.md
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Expand Up @@ -12,14 +12,38 @@ Those can then be used with function calling in a chat or agent.

## Server types

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).
The samples support Stdio, SSE, and Streamable HTTP transports. A Stdio server runs locally, for example by using [npx](https://docs.npmjs.com/cli/v8/commands/npx).

@georgeatparallel George Pickett (georgeatparallel) Sep 5, 2026

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Fixed in 89448fd. The prose now uses SSE consistently; MCPSsePlugin keeps its actual API spelling.


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.

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.
The code shown works the same for an SSE server, only then a MCPSsePlugin needs to be used instead of the MCPStdioPlugin. For Streamable HTTP server, MCPStreamableHttpPlugin can be used.

The reverse, using Semantic Kernel as a server, can be found in the [demos/mcp_server](../../demos/mcp_server/) folder.

### Connecting to a remote Streamable HTTP server

`MCPStreamableHttpPlugin` connects directly to a hosted server without a local server process. For example, [Parallel Search MCP](https://docs.parallel.ai/integrations/mcp/search-mcp) provides `web_search` and `web_fetch` without a Parallel account or API key. Anonymous access is rate limited.

After installing Semantic Kernel as described below, use this inside an async function:

```python
from semantic_kernel import Kernel
from semantic_kernel.connectors.mcp import MCPStreamableHttpPlugin

async with MCPStreamableHttpPlugin(
name="ParallelSearch",
url="https://search.parallel.ai/mcp",
load_prompts=False,
) as mcp_plugin:
kernel = Kernel()
plugin = kernel.add_plugin(mcp_plugin)
print(sorted(plugin.functions))
```

This discovers the server's tools. To let an agent use them, pass `plugins=[plugin]` to the agent and invoke it inside the context manager, as in [the HTTP sample](agent_with_http_mcp_plugin.py). That sample's Azure configuration is still required when using its agent. Omit the plugin from the agent to disable access.

An agent with these tools may call them during its work. Search queries, requested URLs, and any supplied objectives or context are sent to Parallel when tools run.

## Running the samples

1. Depending on the sample you want to run:
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