About Grounder
Grounder is an MCP server that gives local and cloud LLMs current web search results, page content, and cited research sized to a token budget. Its four tools cover quick lookup through open-question research.
Key features
- 01Web search with related queries
- 02Fetch pages as clean Markdown
- 03Token-capped cited evidence packs
- 04Research open questions across sources
Use cases
- 01Check facts against current web results
- 02Read a page within a model's context limit
- 03Collect cited evidence for a research question
MCP support
Grounder exposes four web-grounding tools to MCP clients: web_search, fetch, deep_search, and research. A thin stdio client connects to its hosted service.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
uvx grounder-mcpGROUNDER_API_KEYSecretYour Grounder API key - get one free at https://grounder.devLocal servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
4Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| query | string | Yes | |
| region | string | No |
Details
From the project READMEGrounder 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
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:
{
"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.
fetchreads 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.
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.