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Fetch Agents

Fetch Agents

Local
github.com/SoftwareValeteBrowser Automation0Updated 32w ago

About Fetch Agents

Fetch Agents is an MCP server for AI assistants that analyzes code, compresses context, and searches indexed chat knowledge. It routes requests to cloud-hosted analysis agents over HTTPS.

Key features

  1. 01Extract relevant code sections
  2. 02Map and extract code layers
  3. 03Compress text and conversations
  4. 04Index and search chat history
  5. 05Cloud-based LLM analysis

Use cases

  1. 01Focus on task-relevant parts of large files
  2. 02Summarize lengthy logs and documentation
  3. 03Reduce noise in long conversations
  4. 04Find information from past chats

MCP support

It exposes code analysis, context compression, and knowledge-search tools to MCP-compatible clients. The server forwards requests to cloud-hosted agents over HTTPS.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Fetch Agents MCP Server

MCP server providing AI-powered code analysis, context compression, and knowledge search tools for Claude Code, Cline, and other MCP-compatible AI assistants.

Tools

ToolDescriptionUse When
slim_codeReduce a code file to only sections relevant to your taskReading large files, saving tokens
map_layersList all LAYER sections in a code fileExploring unfamiliar code structure
extract_layerExtract one specific named LAYER sectionFocused reading of a single component
compress_textCompress long text while preserving key informationSummarizing docs, logs, verbose output
analyze_contextFind noise and wasted tokens in conversationsLong conversations hitting context limits
teleport_chatIndex a chat export into searchable knowledge baseReusing insights from past conversations
search_knowledgeSearch indexed knowledge basesFinding relevant past discussions

Quick Start

1. Get a free API key
curl -X POST https://agentslab.duckdns.org/code/register \
  -H "Content-Type: application/json" \
  -d '{"name": "my-project"}'

50 free requests/day included.

2. Add to your MCP config

Claude Code (~/.claude/.mcp.json):

{
  "mcpServers": {
    "fetch-agents": {
      "command": "python",
      "args": ["mcp_server.py"],
      "cwd": "/path/to/fetch-agents-mcp",
      "env": {
        "FETCH_API_KEY": "fak_your_key_here"
      }
    }
  }
}

Cline (VS Code settings):

{
  "cline.mcpServers": {
    "fetch-agents": {
      "command": "python",
      "args": ["mcp_server.py"],
      "cwd": "/path/to/fetch-agents-mcp",
      "env": {
        "FETCH_API_KEY": "fak_your_key_here"
      }
    }
  }
}
3. Install dependencies
pip install mcp httpx

x402 Payments (No API Key Needed)

All endpoints support x402 micropayments on Base (Coinbase L2). Any AI agent with a USDC wallet can pay per-request without registration:

POST /code/slim -> 402 Payment Required + payment-required header
-> Agent signs USDC transfer -> retries with payment header -> gets response

Prices: $0.10 - $1.00 per request depending on tool.

How It Works

The MCP server connects to cloud-hosted agents at agentslab.duckdns.org via HTTPS. Your code is processed server-side using LLM-powered analysis pipelines. No local GPU needed.

Your IDE -> MCP Server -> HTTPS -> Cloud Agents -> Response

Example Usage

Once installed, your AI assistant can use the tools naturally:

"Use slim_code to get only the authentication-related code from this file"

"Compress this error log to the key information"

"Search my teleported knowledge base for how we handled rate limiting"

Pricing

TierPriceIncluded
Free$050 requests/day
Pay-per-use (x402)$0.10-1.00Per request, USDC on Base
Prepaid balanceTop up via cryptoBTC, ETH, USDC, + 10 more
Pro subscription$15/mo300 requests/mo

Requirements

  • Python 3.10+
  • mcp and httpx packages
  • Internet connection (tools call cloud APIs)

License

MIT

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.