GroundRoute

GroundRoute

Web search for AI agents across 6 engines (Serper, Brave, Exa, Tavily, Firecrawl, Perplexity) through one search tool. Routes each query to the cheapest engine that clears a quality bar and caches repeats. Hosted, streamable-HTTP, BYOK supported.

Category
Visit Server

README

<p align="center"> <a href="https://groundroute.ai"> <img src="./assets/banner.svg" alt="GroundRoute, web search MCP server" width="100%"/> </a> </p>

<p align="center"> <a href="https://glama.ai/mcp/servers/PROJECT-B-26/groundroute-mcp"><img src="https://glama.ai/mcp/servers/PROJECT-B-26/groundroute-mcp/badges/score.svg" alt="Glama score"/></a> <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="License: MIT"/></a> <a href="https://www.python.org/"><img src="https://img.shields.io/badge/python-3.12%2B-blue.svg" alt="Python 3.12+"/></a> <a href="https://modelcontextprotocol.io"><img src="https://img.shields.io/badge/MCP-compatible-success.svg" alt="MCP compatible"/></a> <a href="https://smithery.ai/servers/groundroute-ai/web-search"><img src="https://smithery.ai/badge/groundroute-ai/web-search" alt="Smithery"/></a> </p>

Give your AI agent web search across 6 engines through one MCP search tool. Hosted. Routed. Cached.

Why GroundRoute

  • One tool, six engines. Serper, Brave, Exa, Tavily, Firecrawl, Perplexity, behind a single search call. Stop wiring up six APIs, six SDKs, six billing portals.
  • Never more than going direct. Gain-share pricing: you keep ~half of every cache saving, GroundRoute keeps ~half. On a miss, you just pay the engine. BYOK supported.
  • Routing, caching, failover, on by default. Each query goes to the cheapest engine that clears a quality bar. Repeats serve from cache. If an engine degrades, we fall back automatically. No agent code changes.

See it work (5 seconds)

A call to the search tool:

{
  "name": "search",
  "arguments": { "query": "what is RAGflow", "max_results": 3 }
}

The response (trimmed):

{
  "results": [
    {
      "url": "https://ragflow.io/docs/",
      "title": "Quickstart - RAGFlow",
      "snippet": "RAGFlow is an open-source RAG engine based on deep document understanding...",
      "source_engine": "serper"
    },
    {
      "url": "https://github.com/infiniflow/ragflow",
      "title": "RAGFlow is a leading open-source Retrieval-Augmented Generation engine",
      "snippet": "RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine...",
      "source_engine": "serper"
    }
  ],
  "meta": {
    "request_id": "req_abc123",
    "cache_tier": "miss",
    "degraded": false,
    "cost_usd": 0.0021
  }
}

source_engine tells you which engine answered. meta exposes the cache tier and billed cost per call.

Benchmarked, not just shipped

We ran 170 real agent queries across all 6 engines, judged by an LLM, to map cost vs. quality per query class. Full methodology and per-engine results: State of AI Search.


Install

The hosted endpoint is https://api.groundroute.ai/mcp (streamable-HTTP). Get an API key at groundroute.ai/keys.

Claude Desktop / Claude Code, add to your MCP config:

{
  "mcpServers": {
    "groundroute": {
      "type": "http",
      "url": "https://api.groundroute.ai/mcp",
      "headers": { "Authorization": "Bearer gr_YOUR_KEY" }
    }
  }
}

Cursor, ~/.cursor/mcp.json:

{ "mcpServers": { "groundroute": { "url": "https://api.groundroute.ai/mcp",
  "headers": { "Authorization": "Bearer gr_YOUR_KEY" } } } }

VS Code (native MCP / Continue), .vscode/mcp.json:

{ "servers": { "groundroute": { "type": "http", "url": "https://api.groundroute.ai/mcp",
  "headers": { "Authorization": "Bearer gr_YOUR_KEY" } } } }

Local / stdio-only clients, bridge stdio to HTTP with mcp-remote:

{ "mcpServers": { "groundroute": {
  "command": "npx",
  "args": ["-y", "mcp-remote", "https://api.groundroute.ai/mcp", "--header", "Authorization:Bearer gr_YOUR_KEY"]
} } }

Run this repo's stdio server (optional)

This repo also ships a small native stdio MCP server (server.py) that forwards to the hosted API, useful for stdio-only clients or containerized runs.

pip install -r requirements.txt
GROUNDROUTE_API_KEY=gr_YOUR_KEY python server.py

Or with Docker:

docker build -t groundroute-mcp .
docker run -i -e GROUNDROUTE_API_KEY=gr_YOUR_KEY groundroute-mcp

Introspection (tool discovery) works with no key; running a search requires GROUNDROUTE_API_KEY (get one at https://groundroute.ai/keys).

The search tool

Param Type Notes
query string required
mode enum auto (default), web, news, academic, answer, page
max_results integer default 10, max 50
freshness enum fresh, semi, static; omit to auto-detect
domains string[] include-only domain filter, e.g. ["arxiv.org"]
lang string ISO 639-1 language code, e.g. en
country string ISO 3166-1 alpha-2 country code, e.g. us

Returns a structured result: ranked results (url / title / snippet / content / source_engine / published_at), an optional synthesized answer with citations (answer mode), and meta (request_id / cache_tier / degraded / cost_usd). Routed, cached, and reliable.

How it works

One endpoint in front of many search engines, with price-led routing, caching, failover, and usage governance. See the docs and the State of AI Search benchmark (170 real agent queries across all 6 engines).

Links

  • Homepage: https://groundroute.ai
  • Get a key: https://groundroute.ai/keys
  • Playground (try without installing): https://groundroute.ai/playground
  • Docs: https://groundroute.ai/docs/mcp-server

registry-manifest.json in this repo is the listing manifest for MCP registries.

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
Qdrant Server

Qdrant Server

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

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