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
- 01Extract relevant code sections
- 02Map and extract code layers
- 03Compress text and conversations
- 04Index and search chat history
- 05Cloud-based LLM analysis
Use cases
- 01Focus on task-relevant parts of large files
- 02Summarize lengthy logs and documentation
- 03Reduce noise in long conversations
- 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.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEFetch 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
| Tool | Description | Use When |
|---|---|---|
| slim_code | Reduce a code file to only sections relevant to your task | Reading large files, saving tokens |
| map_layers | List all LAYER sections in a code file | Exploring unfamiliar code structure |
| extract_layer | Extract one specific named LAYER section | Focused reading of a single component |
| compress_text | Compress long text while preserving key information | Summarizing docs, logs, verbose output |
| analyze_context | Find noise and wasted tokens in conversations | Long conversations hitting context limits |
| teleport_chat | Index a chat export into searchable knowledge base | Reusing insights from past conversations |
| search_knowledge | Search indexed knowledge bases | Finding 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
| Tier | Price | Included |
|---|---|---|
| Free | $0 | 50 requests/day |
| Pay-per-use (x402) | $0.10-1.00 | Per request, USDC on Base |
| Prepaid balance | Top up via crypto | BTC, ETH, USDC, + 10 more |
| Pro subscription | $15/mo | 300 requests/mo |
Requirements
- Python 3.10+
mcpandhttpxpackages- Internet connection (tools call cloud APIs)
License
MIT
Comments
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.