MCP.so
ログイン

AWS MCP Server with Agno Agent

@skjortan23

リポジトリ

AWS MCP Server with Agno Agent について

Query your AWS environment with natural language.

基本情報

カテゴリ

クラウドとインフラ

ランタイム

python

トランスポート

stdio

公開者

skjortan23

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "aws-security-mcp-server": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is AWS MCP Server with Agno Agent?

It is an MCP (Model Context Protocol) server that integrates AWS tools (listing security groups, S3 buckets, analyzing VPC connections) using FastMCP and the Agno framework. It enables remote clients to interact with these AWS operations via standardized MCP endpoints and includes an interactive agent that uses local Ollama models.

How to use AWS MCP Server with Agno Agent?

After installing Python 3.11+, configuring AWS credentials, and installing Ollama, clone the repository and install dependencies. Run the MCP server with python src/aws-security-mcp-server.py. Then run the interactive agent with python src/aws-demo-agent.py. Alternatively, connect any MCP-compliant client (e.g., using Agno’s MCPTools) to the SSE endpoint at http://127.0.0.1:8000/sse/.

Key features of AWS MCP Server with Agno Agent

  • AWS Security Group Tool: Lists security groups with inbound/outbound rules.
  • AWS S3 Bucket Tool: Lists S3 buckets with region, creation date, and access policy.
  • AWS VPC Connections Tool: Analyzes VPC peering, endpoints, transit gateways, route tables, and network ACLs.
  • MCP Server with SSE transport: Real‑time communication for MCP‑compliant clients.
  • Interactive Agno Agent: Task management, command history, and real‑time tool call streaming.
  • Ollama Integration: Uses local LLMs, reducing dependency on external APIs.

Use cases of AWS MCP Server with Agno Agent

  • Query AWS security groups across regions from any MCP client.
  • List and inspect S3 bucket properties and policies.
  • Analyze VPC network connections including peering, endpoints, and route tables.
  • Build custom AI agents that can manage AWS infrastructure.
  • Run parallel AWS queries in a terminal with real‑time streaming.

FAQ from AWS MCP Server with Agno Agent

What dependencies are required?

Python 3.11+, AWS credentials configured for boto3, Ollama installed locally, and Python packages: boto3, agno, fastmcp, rich, click.

How does the server communicate with clients?

It uses FastMCP with Server‑Sent Events (SSE) transport, listening on 127.0.0.1:5678 (the SSE URL is typically http://127.0.0.1:8000/sse/).

Can I use external AI models instead of Ollama?

The project demonstrates only local Ollama integration. Using other models would require modifying the agent code.

What AWS actions can the server perform?

It can list security groups, list S3 buckets, and analyze VPC connections (peering, endpoints, transit gateways, route tables, network ACLs). All operations use boto3 with the configured AWS credentials.

Is this project open source and free?

Yes, it is licensed under the MIT License (described as “Free for all!”).

コメント

「クラウドとインフラ」の他のコンテンツ

Supabase MCP Server

supabase-community

Connect Supabase to your AI assistants

2.8K

container-use

aluzzardi

Development environments for coding agents. Enable multiple agents to work safely and independently with your preferred stack.

3.9K

Run Model Context Protocol (MCP) servers with AWS Lambda

awslabs

Run existing Model Context Protocol (MCP) stdio-based servers in AWS Lambda functions

373

K8s MCP Server

alexei-led

K8s-mcp-server is a Model Context Protocol (MCP) server that enables AI assistants like Claude to securely execute Kubernetes commands. It provides a bridge between language models and essential Kubernetes CLI tools including kubectl, helm, istioctl, and argocd, allowing AI syste

212

container-use

dagger

Development environments for coding agents. Enable multiple agents to work safely and independently with your preferred stack.

3.9K