Mcp Server
About
<p align="center"> <a href="https://pypi.org/project/fast-agent-mcp/"><img src="https://img.shields.io/pypi/v/fast-agent-mcp?color=%2334D058&label=pypi" /></a> <a href="#"><img src="https://github.com/evalstate/fast-agent/actions/workflows/main-checks.yml/badge.svg" /></a> <a…
Details
- License
- Apache-2.0 license
Explore
- First framework with end‑to‑end MCP Sampling support.
- Supports Anthropic (Haiku, Sonnet, Opus) and OpenAI models.
- Multi‑modal: handles images and PDFs via prompts, resources, and MCP tools.
- Declarative agent and workflow definitions in simple files.
- Built‑in workflow patterns: Chain, Parallel, Evaluator‑Optimizer, Router, Orchestrator.
- Agents can request human input for additional context.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Mcp ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install via uv pip install fast-agent-mcp, then run uv run fast-agent setup to create example config files. Define agents with Python decorators like @fast.agent and invoke them with await agent("message") or open an interactive chat with await agent.interactive(). Use the --model flag to specify a model (e.g., uv run sizer.py --model sonnet).
LLM APIs have restrictions on the content types that can be returned as Tool Calls/Function results via their Chat Completions API's:
- OpenAI supports Text
- Anthropic supports Text and Image
For MCP Tool Results, ImageResources and EmbeddedResources are converted to User Messages and added to the conversation.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp server": {
"mcp-server-ernestocullari": {
"command": "uv",
"args": [
"pip",
"install",
"fast-agent-mcp",
"#",
"install",
"fast-agent!"
]
}
}
}
}McpServers
{
"mcp-server-ernestocullari": {
"command": "uv",
"args": [
"pip",
"install",
"fast-agent-mcp",
"#",
"install",
"fast-agent!"
]
}
}Combining Agents and using MCP Servers
_To generate examples use fast-agent quickstart workflow. This example can be run with uv run workflow/chaining.py. fast-agent looks for configuration files in the current directory before checking parent directories recursively._
Agents can be chained to build a workflow, using MCP Servers defined in the fastagent.config.yaml file:
@fast.agent(
"url_fetcher",
"Given a URL, provide a complete and comprehensive summary",
servers=["fetch"], # Name of an MCP Server defined in fastagent.config.yaml
)
@fast.agent(
"social_media",
"""
Write a 280 character social media post for any given text.
Respond only with the post, never use hashtags.
""",
)
@fast.chain(
name="post_writer",
sequence=["url_fetcher", "social_media"],
)
async def main():
async with fast.run() as agent:
# using chain workflow
await agent.post_writer("http://llmindset.co.uk")
All Agents and Workflows respond to .send("message") or .prompt() to begin a chat session.
Saved as social.py we can now run this workflow from the command line with:
uv run workflow/chaining.py --agent post_writer --message "<url>"
Add the --quiet switch to disable progress and message display and return only the final response - useful for simple automations.
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