Web Search

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.

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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

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.