Enables applications to provide context for LLMs in a standardized way, separating the concerns of providing context from LLM interaction.
Enables applications to provide context for LLMs in a standardized way, separating the concerns of providing context from LLM interaction. Exposed over MCP by the mcp python sdk mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
which python python3 -m venv myenv source myenv/Scripts/activate pip install -r requirements.txt python fast_mcp_server.py
The Model Context Protocol allows applications to provide context for LLMs in a standardized way, separating the concerns of providing context from the actual LLM interaction. This Python SDK implements the full MCP specification, making it easy to:
The server ships on PyPI as mcp, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
Everything the assistant can do here goes through one of these:
Server — The FastMCP server is your core interface to the MCP protocol. It handles connection management, protocol compliance, and message routing:Resources — Resources are how you expose data to LLMs. They're similar to GET endpoints in a REST API - they provide data but shouldn't perform significantTools — Tools let LLMs take actions through your server. Unlike resources, tools are expected to perform computation and have side effects:Prompts — Prompts are reusable templates that help LLMs interact with your server effectively:Images — FastMCP provides an Image class that automatically handles image data:Context — The Context object gives your tools and resources access to MCP capabilities:You will need one environment variable: OPENAI_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. MCP Python SDK's toolset — Server, Resources, Tools and 3 more — is a fair guide to whether it matches your workflow. It is maintained by ujjalcal; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| Server | The FastMCP server is your core interface to the MCP protocol. It handles connection management, protocol compliance, and message routing: |
| Resources | Resources are how you expose data to LLMs. They're similar to GET endpoints in a REST API - they provide data but shouldn't perform significant computation or have side effects: |
| Tools | Tools let LLMs take actions through your server. Unlike resources, tools are expected to perform computation and have side effects: |
| Prompts | Prompts are reusable templates that help LLMs interact with your server effectively: |
| Images | FastMCP provides an Image class that automatically handles image data: |
| Context | The Context object gives your tools and resources access to MCP capabilities: |
{
"mcpServers": {
"python-sdk": {
"command": "uvx",
"args": ["mcp"],
"env": {
"OPENAI_API_KEY": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.