Fetch Agents MCP Server

@SoftwareValete

代码仓库

基本信息

分类

浏览器自动化

传输方式

stdio

作者

SoftwareValete

提交者

Хугин и Мунин

关于 Fetch Agents MCP Server

AI-powered code analysis and context optimization tools. Slim large code files to relevant sections (save 60-80% tokens), compress text, analyze conversations for noise, and search past chat knowledge bases. Supports x402 micropayments on Base.

#code-analysis#context-compression#token-optimization #code-search #knowledge-base #x402 #ai-tools #developer-tools

Fetch Agents 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": {
    "fetch-agents": {
      "command": "python",
      "args": [
        "mcp_server.py"
      ],
      "cwd": "/path/to/fetch-agents-mcp"
    }
  }
}

Fetch Agents MCP Server 工具

Reduce a code file to only sections relevant to your task

List all LAYER sections in a code file

Extract one specific named LAYER section

Compress long text while preserving key information

Find noise and wasted tokens in conversations

Index a chat export into searchable knowledge base

Search indexed knowledge bases

概览

What is Fetch Agents?

Fetch Agents is an MCP server that provides AI-powered code analysis, context compression, and knowledge search tools for Claude Code, Cline, and other MCP-compatible AI assistants. It connects to cloud-hosted agents via HTTPS and processes code server-side using LLM pipelines.

How to use Fetch Agents?

Obtain a free API key by registering via curl (50 free requests/day included). Add the server to your MCP config with the python command, the script mcp_server.py, and the API key in environment variables. Install dependencies with pip install mcp httpx. Optionally use x402 micropayments to pay per request without registration.

Key features of Fetch Agents

Reduce code files to only relevant sections with slim_code
List and extract LAYER sections from code files
Compress long text while preserving key information
Analyze conversations for noise and wasted tokens
Index chat exports into a searchable knowledge base
Search indexed knowledge bases for past discussion insights
Pay-per-use via x402 micropayments on Base

Use cases of Fetch Agents

Reading large code files while saving token usage
Exploring unfamiliar code structure quickly
Summarizing long documentation or verbose logs
Reusing insights from past conversations in new chats
Identifying wasted tokens in long conversations hitting context limits

常见问题

How does Fetch Agents process my code?

Your code is sent via HTTPS to cloud agents at `agentslab.duckdns.org` where LLM-powered analysis pipelines run. No local GPU is needed.

What are the runtime requirements?

Python 3.10+, the `mcp` and `httpx` packages, and an internet connection are required.

How much does Fetch Agents cost?

A free tier includes 50 requests per day. Pay-per-use costs $0.10–$1.00 per request via USDC on Base (x402). Prepaid balances and a $15/month Pro subscription (300 requests/month) are also available.

How do I authenticate with the server?

You can either use a free API key obtained from the registration endpoint or use x402 micropayments by signing a USDC transfer on Base per request (no API key needed).

Where is my data processed?

Data is processed on cloud servers. The README does not specify long-term storage or deletion policies.

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