grounder-mcp

grounder-mcp

Gives local and cloud LLMs live web grounding as four MCP tools: web_search, fetch, deep_search (a token-capped, cited evidence pack sized to a small context window), and research (an agentic search-and-read loop). Flat monthly pricing, no query content stored.

Category
Visit Server

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.

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

<!-- mcp-name: io.github.rozetyp/grounder -->

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured