Grounder
Web grounding for local & cloud LLMs: search, fetch, token-capped evidence packs, and research.
Open source Open in the app JSON README (API)
About
Web grounding for local & cloud LLMs: search, fetch, token-capped evidence packs, and research.
Details
- Kind
- MCP servers
- Topic
- No topic detected
- Publisher
- rozetyp
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 0.4.1
- Last push
- 2026-08-13T14:27:29Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-29 04:01:19
- Updated
- 2026-08-29 04:01:19
- Origin id
io.github.rozetyp/grounder
README
# 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.
A thin stdio client for the hosted service at https://grounder.dev - no browser, no scraper,
nothing heavy runs locally. It runs with **no signup** on a shared demo key; add your own free key
(1,500 pages/month, no card) at grounder.dev for real use.
## Install
```bash
uvx grounder-mcp # or: pip install grounder-mcp
```
## Configure
Claude Desktop, Cursor, LM Studio, Continue.dev, or any MCP client. The key is **optional** - omit
it to try Grounder on the shared demo key, or add your own free key from grounder.dev:
```json
{
"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 people-also-ask, related searches, and the knowledge graph. The top snippet often already holds the answer. |
| `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, ranked into a token-capped, cited evidence pack. Optional grounded answer. |
| `research` | Investigates an open question with no ready-made answer: it plans, reads primary sources, notices what is missing, goes back for it, and reasons to a grounded conclusion. |
## 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 pages can be 20,000+ tokens (we measured 22,759
for one query) - enough to make a small model return nothing. You get the relevant passages, not
whole pages.
- **The live page, not a cached copy.** `fetch` reads the actual current page; any caching is short,
timestamped, and force-refreshable.
- **Flat monthly price, billed in pages.** You only pay for pages actually delivered. No per-call metering.
- **No query content stored.** Ever.
## Pricing
Free: 1,500 pages/month, no card. Starter $9/mo, Pro $19/mo. Full table at https://grounder.dev/pricing.
<!-- mcp-name: io.github.rozetyp/grounder -->