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MCP Server

@ernestocullari

代码仓库

基本信息

分类

其他

许可证

Apache-2.0 license

传输方式

stdio

作者

ernestocullari

关于 MCP Server

暂无概览

#mcp-server#agent-framework#workflow-automation

MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-server-ernestocullari": {
      "command": "uv",
      "args": [
        "pip",
        "install",
        "fast-agent-mcp",
        "#",
        "install",
        "fast-agent!"
      ]
    }
  }
}

MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Mcp Server?

Mcp Server is a framework for creating and interacting with sophisticated AI Agents and Workflows, with complete, end-to‑tested support for the Model Context Protocol (MCP) including Sampling. It supports both Anthropic and OpenAI models, is multi‑modal (images and PDFs), and uses a simple declarative syntax stored in configuration files.

How to use Mcp Server?

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).

Key features of Mcp Server

  • 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.

Use cases of Mcp Server

  • Building a chain of agents that fetch and summarize content from the web.
  • Creating a multi‑language translator using parallel agent execution.
  • Generating and refining reports with an Evaluator‑Optimizer loop.
  • Routing complex user requests to the most capable agent using an LLM‑driven router.
  • Orchestrating multiple agents to break down and complete large tasks.

常见问题

1

What dependencies does Mcp Server require?

Mcp Server requires Python and the `uv` package manager. Install the package with `uv pip install fast-agent-mcp`.

2

What models can be used with Mcp Server?

Both Anthropic models (Haiku, Sonnet, Opus) and OpenAI models (gpt‑4o/gpt‑4.1 family, o1/o3 family) are supported. Models can be specified per agent or via the `--model` command‑line flag.

3

Where does Mcp Server store agent configurations?

Prompts and configurations are stored in simple files (e.g., `fastagent.config.yaml`). The framework looks for these files in the current directory and then recursively in parent directories.

4

Can Mcp Server use external MCP servers?

Yes, agents can be connected to MCP servers by listing their names in the `servers` parameter of the agent decorator. Servers must be defined in the configuration file.

5

Does Mcp Server support multimodal input?

Yes, both Anthropic and OpenAI endpoints support images and PDFs via prompts, resources, and MCP tool call results.

评论

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