As the AI Agents ecosystem and usage grow, we at SerpApi are looking to support that effort and simplify how AI developers can get fast and reliable access to the latest Google and other Search web data sources. Today, we are releasing an open-source Model Context Protocol (MCP) server that exposes our web search API to AI agents and developers through a familiar and common interface.

This server lets any MCP-compatible AI model client call SerpApi’s search tools without writing any custom code or dealing with complicated SDKs. The agent can ask the server what tools it offers and then invoke them as needed to achieve the desired outcome. SerpApi’s MCP server includes a unified search tool so that LLM (Large Language Models) agents can retrieve real-time search results through simple function calls. The search tool is flexible and supports all schemas and parameters that are supported in our REST API.

The server supports multiple engines (Google, Bing, Yahoo, DuckDuckGo, Yandex, Baidu, YouTube, eBay, Walmart, and more) and specialised queries such as weather or stock lookups, returning clean JSON results optimised for AI application consumption. You may register for SerpApi to claim free credits.

What is the Model Context Protocol (MCP)?


The Model Context Protocol (MCP) is an open protocol created by Antrophic to let AI models operate in richer, more context-aware ways inside real-world development environments and applications. Instead of relying solely on static training data, MCP lets models access live information—such as local files in a file system, project metadata, or connected external systems—all through a unified, standardised protocol. It works over simple transports like stdio, sse, or even USB-C (when implemented over serial-style connections), making it easy to embed into many platforms. To support broad interoperability, MCP also clarifies how transport methods such as server-sent events or process pipes can carry messages between tools and clients. MCP is defined through a clear protocol specification built on JSON-RPC, and many implementations explicitly align with JSON-RPC 2.0, ensuring that tools and clients can communicate reliably without vendor lock-in. As adoption grows, MCP aims to function as a truly standardized protocol that any AI system can use—whether it’s ChatGPT from OpenAI, Claude from Anthropic, Gemini, or any other model—so long as they connect through a compatible endpoint. These foundations also help address security risks by enabling structured access control, controlled sandboxing, and predictable interactions between models and tools.

With MCP, developers can build powerful MCP tools that expose datasets, APIs, services, or an entire workspace to AI assistants and chatbots. This enables models to read docs, run development tools, inspect code, or even debug problems interactively using natural language while still following strict structured messaging. Because everything follows the same protocol, tools become reusable across editors, terminals, and other apps. This growing MCP integration ecosystem means that not only OpenAI models but also third-party AI systems can participate in shared workflows. MCP supports emerging agentic AI patterns, where models autonomously sequence many small tool calls to complete complex use cases, while still respecting boundaries around sensitive data and maintaining scalability as environments grow. With these enhancements, MCP turns AI from a passive text generator into an active participant that can interact with your environment in a safe, structured way—unlocking smarter automation, deeper integrations, and more capable AI-assisted workflows.

Key Features

  • Multi-Engine Search: A single search tool covers dozens of engines that are providers of external data sources—Google (full and light), Bing, Yahoo, DuckDuckGo, Yandex, Baidu, YouTube, eBay, Walmart, and others.
  • Structured Results: The server autogenerates structured JSON block templates (external tools such as answer boxes, knowledge graph data, calculations, and more) along with organic, news, image, and shopping results. AI Assistant Agents get exactly what they need without manual parsing.
  • Raw and Clean Output: Developers can request raw JSON or a clean, normalised version with fields like title, snippet, link, etc.
  • Robust Reliability: Error handling and retry logic are included by default. Authentication failures, rate limits, and network timeouts return helpful messages that agents can use to respond gracefully.
  • Open Source: We at SerpApi believe that open source is an integral part of the development of AI Tools. Anyone with some Python knowledge should be able to contribute to Artificial Intelligence tooling and general AI Systems. You may head to our GitHub Repository to access an open-sourced version of the MCP Server.

Getting Started

You can start by using our remotely hosted MCP Server at https://mcp.serpapi.com/SERPAPI_API_KEY/mcp. The MCP server supports requests from any origin.

You can also self-host the MCP server locally to have full control of the deployment. You can start with it by cloning the repository and installing the dependencies:

git clone https://github.com/serpapi/serpapi-mcp.git
cd serpapi-mcp/
uv sync

Set your SerpApi API key in a .env file:

SERPAPI_API_KEY=your_key_here

Then start the server:

uv run src/server.py

Configure your MCP-client to connect to the server. Once connected, the AI-powered agent will detect SerpApi’s search tool and any additional resources the server provides in automation.

You can also use the provided Docker configuration to deploy the service as a container.

Interacting with the server

You can use the MCP inspector to interact and explore the MCP service. You can start by installing the package:

npm install -g  @modelcontextprotocol/inspector

Then start the inspector:

npx @modelcontextprotocol/inspector

The service will become available on localhost:6274. You can try listing the available tools, see their description and parameters.

You can also test the MCP server inside Microsoft’s VS Code IDE. You can add the MCP server config to the .vscode/mcp.json file and test it inside the VS Code Copilot.

{
  "servers": {
    "serpapi-mcp": {
      "type": "http",
      "url": "https://mcp.serpapi.com/<API_KEY>/mcp"
    }
  }
}

Accelerating AI Integration

By adopting the MCP standard, SerpApi makes web search a native capability for AI agents. Instead of bespoke integration code with complicated connectors, agents discover and use SerpApi’s tools automatically. This reduces hallucinations and accelerates the development of agentic applications.

SerpApi releases this server to give AI developers fast, reliable access to live search data. We’re excited to support the growing ecosystem of AI agents and provide the infrastructure that helps them understand the real world with open standards and with minimal permissions needed. The MCP server is open-source, and we invite developers to explore it, contribute, and integrate it into their own AI workflows.

Try out the SerpApi MCP server and see how it fits into your agent stack.