Fully decentralized P2P search engine for LLMs via MCP
Infomesh MCP server exists for a simple reason — assistants are far more useful when they can act on Infomesh directly instead of describing what you should do. Fully decentralized P2P search engine for LLMs via MCP.
Fully Decentralized P2P Search Engine for LLMs
Once Infomesh is connected, these are the calls the assistant has available:
Feature — DescriptionExamples — Ready-to-run Python scripts are available in the examples/ directory:You will need one environment variable: PEER_ID. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Being a remote server, there is no local install. You register the endpoint with your client, authorise it once, and the tools appear.
Plenty of search and retrieval servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Infomesh's toolset — Feature, Examples — is a fair guide to whether it matches your workflow. It is maintained by InfoMesh Contributors; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| Feature | Description |
| Examples | Ready-to-run Python scripts are available in the [examples/](examples/) directory: |
{
"mcpServers": {
"infomesh": {
"command": "uvx",
"args": ["infomesh", "mcp"]
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| PEER_ID | Configuration value read at startup. | Optional |
The simplest web tool that matters — fetch any URL and get model-ready markdown back.
Industrial-strength web extraction — render, scrape, crawl and search entire sites into clean markdown.
Puppeteer-powered browser control that drives pages from the accessibility tree instead of pixels.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Put 6,000+ pre-built scrapers at your assistant's fingertips through one MCP endpoint.