Grounder MCP

Live web grounding for local and cloud LLMs, as four MCP tools. Every model is frozen at its training cutoff; Grounder gives yours the current web - ranked results, real page content, and a cited evidence pack sized to your context window.

This is a thin stdio client for the hosted service at grounder.dev - no browser, no scraper, nothing heavy runs locally. Get a key (free, no card) at grounder.dev.

Install

uvx grounder-mcp          # or: pip install grounder-mcp

Configure

Claude Desktop, Cursor, LM Studio, Continue.dev, or any MCP client:

{
  "mcpServers": {
    "grounder": {
      "command": "uvx",
      "args": ["grounder-mcp"],
      "env": { "GROUNDER_API_KEY": "gnd_live_your_key" }
    }
  }
}

The four tools

Tool	What it does
`web_search`	Google organic results plus the surfaces around them - people-also-ask, related searches, knowledge graph. The top snippet often already holds the answer, so the model can skip a fetch.
`fetch`	One page as clean markdown, capped to your token budget, plus the final URL after redirects.
`deep_search`	One search, read across the pages it surfaces, chunked and ranked into a token-capped, cited evidence pack. Optional grounded answer, written only from what it read.
`research`	Investigates an open question with no ready-made answer - the kind you'd hand an analyst: it plans, reads primary sources, notices what is still missing, goes back for it, and answers only from the pages it actually read.

Why use it

It fits a small context window. Results come back token-capped, so they slot into an 8-32k local model instead of overflowing it. A few raw web pages can be 20,000+ tokens - we measured 22,759 for one query - which is enough to make a small model return nothing at all. deep_search and research hand back the relevant passages, not whole pages.
The live page, not a cached copy. fetch reads the actual current page on request. Any caching is short, timestamped, and force-refreshable - it never turns into an opaque stale index.
Flat monthly price, billed in pages. One page is one search, one fetch, or one page a deep_search reads, and you only pay for pages actually delivered - an empty search or an unreadable page is free. No per-call metering.
No query content stored. Ever.
Nothing heavy to run. The client just forwards tool calls to the hosted API, so it installs in seconds with no browser or scraping stack on your machine.

Pricing

Free: 1,500 pages/month, email only, no card. Starter $9/mo and Pro $19/mo add higher page budgets, protected-page access, and more research runs. Full table at grounder.dev/pricing.

Links

Site and docs: grounder.dev · docs · guides
MCP registry: io.github.rozetyp/grounder
PyPI: grounder-mcp

This server cannot be installed

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license - not tested

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quality - not tested

C

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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