Nimble for AI: MCP

Fuel AI Assistants With Fresh Web Data via Nimble MCP

Nimble gives AI assistants the ability to perceive, reason, and act using live, structured web data instantly available via our hosted MCP Server.

 AI Apps such as Claude, Qodo, Cursor, and CoPilot, or enhance custom Web Agents on frameworks including LangChain, LlamaIndex.

The Data Backbone for
AI Agents and LLMs

Nimble’s Model Context Protocol (MCP) server acts as a universal bridge between AI systems and the live internet.

The agent asks a question over MCP

(e.g. “What are the top trending electric cars in Europe this week?”)

Nimble performs real-time search across the web

Search engines and target websites using Browserless drivers

Structured results returned in seconds

Ready-to-use JSON data, compatible with any AI assistant or framework

Your model reasons with fresh, real-world data

Nimble’s Model Context Protocol (MCP) server acts as a universal bridge between AI systems and the live internet.No stale inputs. No hallucinations.

Enabling Your Agents to Search, Extract, and Structure the Web

Nimble’s MCP infrastructure includes a suite of tools designed to support intelligent, autonomous reasoning:

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nimble_deep_web_search

Scrape real-time web content from major search engines

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nimble_extract

Extract content from a specific URL

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nimble_google_maps_search

Discover and analyze local businesses

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nimble_google_maps_reviews

Retrieve detailed customer reviews

Seamlessly Connect Leading AI Apps and Agent Frameworks with Nimble MCP

Connect Your AI Stack
to the Web in Minutes

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CLI and Python SDK

Get started in minutes with simple command-line tools and async Python clients that make calling MCP endpoints straightforward.

Python

Prebuilt integrations

Nimble's MCP server connects effortlessly to popular AI platforms such as Claude Desktop, Cursor, qodo, CoPilot, OpenAI Playground, and others —no custom setup required.

Nimble to any AI Apps such as Claude, Qodo, Cursor, and CoPilot, or enhance custom Web Agents on frameworks including LangChain, LlamaIndex, Agno, Autogen, etc.
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End-to-end examples

The Nimble Cookbook offers hands-on guides that show you how to use MCP data for real use cases like LLM grounding, real-time web extraction, and AI workflow automation.

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Get Started Quickly with
Nimble’s MCP Cookbook

Python
MCP_URL = "https://mcp.nimbleway.com/sse"
async def search_web(query: str) -> None:
    api_key = os.environ.get("NIMBLE_API_KEY")
    if not api_key:
        raise ValueError("NIMBLE_API_KEY environment variable not set")
    transport = SSETransport(
        MCP_URL, 
        headers={"Authorization": f"Basic {api_key}"}
    )
    client = Client(transport)
    async with client:
        results = await client.call_tool(
            "nimble_deep_web_search", 
            {"query": query}
        )
        for result in results:
            if hasattr(result, "text"):
                print(result.text)
if __name__ == "__main__":
    query = sys.argv[1] if len(sys.argv) >
 1 else "recent advances in artificial intelligence"
 asyncio.run(search_web(query))
Get a Demo

The Fastest Path to Web-Connected AI

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MCP scraping at scale

Headless browsers render JS, extract key fields, and return accurate data fast

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Zero setup

Use Nimble’s hosted MCP Server – no need to install locally

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Structured data for LLMs

AI-ready schema with every result

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LLM integration-ready

Works natively with OpenAI, Anthropic, Claude, LangChain, and more

One Integration. Infinite Context.

Whether you're crafting your first AI assistant or scaling a production-grade agentic system, Nimble delivers the real-time web context your models need to reason, act, and adapt.