Autom MCP: Give Claude, Cursor, and Gemini Live SERP Data

The gap in every AI assistant

Claude, Cursor, and Gemini are powerful. But they have a hard cutoff: their knowledge ends where their training data ends. Ask them what's ranking on Google today, which competitors just published a new page, or what news is breaking about your industry — and you get one of two things: a polite refusal, or a confident answer based on months-old data.

The Model Context Protocol (MCP) is the bridge between AI assistants and live data. Autom's MCP server puts every SERP endpoint — Google, Bing, Brave, Google News, Google Images, Google Shopping, Google Jobs, Google Maps, and more — directly inside your AI tool as callable functions.

Your assistant doesn't just know things. It can look them up.

What MCP actually is

MCP is an open standard that lets AI assistants call external tools during a conversation or task. Instead of the assistant guessing, it calls a function, gets a structured response, and reasons about real data.

Autom's MCP server exposes the full Autom API as MCP tools. Every SERP endpoint becomes a function the AI can invoke. The setup is a single URL and an API key — no infrastructure, no proxy management, no parsing.

MCP endpoint: https://mcp.autom.dev/
Authentication: x-api-key header

Once connected, your AI assistant can search Google, pull Bing results, scan news headlines, or check shopping listings — mid-conversation, mid-task, mid-code-generation.

Setup in under two minutes

Claude Code

claude mcp add --transport http autom https://mcp.autom.dev/ \
  --header "x-api-key: YOUR_API_KEY"

Cursor

Add to your project's mcp.json (Settings → MCP → Add HTTP server):

{
  "mcpServers": {
    "autom": {
      "url": "https://mcp.autom.dev/",
      "headers": {
        "x-api-key": "YOUR_API_KEY"
      }
    }
  }
}

Gemini CLI

Point the MCP client at https://mcp.autom.dev/ and pass x-api-key as the tool authentication header. See the Gemini CLI MCP documentation for the exact config format.

OpenAI Developer Mode

Wire MCP tools to https://mcp.autom.dev/ with your API key following the OpenAI Developer Mode guide.

Get your API key at app.autom.dev. Never commit it to a repository — store it in an environment variable or a credentials manager.

What your AI can do once connected

Real-time competitive research

You're asking Claude to write a content brief. Instead of guessing what competitors are publishing, it calls Google Search:

"Let me check what's currently ranking for that keyword before I structure this brief."

It pulls the top 10 results, reads the titles and snippets, and produces a brief that reflects the actual SERP landscape — not training data from a year ago.

SEO analysis grounded in live data

You're working in Cursor on an SEO script. Your assistant pulls the current ranking position for a target keyword, cross-references it with Bing, and suggests changes based on what's actually happening in the index — not what used to work.

News monitoring and summarization

An AI assistant tasked with daily briefings can call Google News on a topic, get the latest headlines, and draft a summary — no browser needed, no manual copying.

Tool: google_news
Query: "AI regulation Europe"
→ Returns: 10 latest articles with title, source, date, snippet
→ AI summarizes: key developments from the last 24 hours

Shopping and pricing intelligence

Need to know where a product sits in Google Shopping before writing copy? The assistant calls the endpoint, gets current listings with prices and merchants, and uses that data directly in the task.

Lead enrichment on the fly

While working on an outreach campaign, your AI can call Google Maps or Google Search with a location parameter to pull local businesses in a niche — without leaving the conversation.

Why Autom and not a DIY scraper

Building SERP scraping yourself isn't a weekend project. Here's what you're actually signing up for:

The real cost of DIY isn't the code you write on day one — it's the constant maintenance as search engines update their anti-bot measures, HTML structure, and rate limiting policies. That maintenance is invisible until it breaks at the worst possible moment.

Autom handles all of it. Your AI tool gets clean, structured JSON every time.

Reliability where it counts

When your AI assistant calls a SERP tool mid-task, a failed request breaks the workflow. Autom's infrastructure is built for exactly this pattern:

• Median response time under 2 seconds — fast enough for interactive use inside Cursor or Claude Code
• Only successful calls are billed — if a request fails for any reason, you pay nothing. Retries are safe.
• Consistent schema across all engines — organic_results, pagination, search_parameters are the same top-level keys whether the tool calls Google, Bing, or Brave. Your AI doesn't need special handling per engine.
• No result hallucination — the AI is calling a real endpoint and reading real data, not filling in gaps from training data

That last point matters more than it might seem. When an AI assistant generates SERP data from memory, it's essentially hallucinating rankings. With the MCP server, the data is fetched, not imagined.

The full tool surface available in your AI assistant

Every Autom endpoint becomes a callable tool via MCP. All share the same x-api-key authentication and cost 1 credit per call:

In a single Claude conversation, you could: search Google for competitor rankings, cross-check on Bing, pull the latest news on the topic, and check Google Shopping for product-intent context — all without leaving the chat.

Combining engines for stronger signals

Running the same query across Google, Bing, and Brave from inside your AI assistant gives you cross-index validation that no single-engine approach can match:

• A URL appearing in the top 5 on all three engines is a strong authority signal
• A URL ranking on Bing but not Google reveals indexation gaps worth investigating
• Brave's independent crawler often surfaces content that Google and Bing have deprioritized

Your AI assistant can reason about these differences directly when you give it access to all three. That's not possible when results come from training data.

Practical examples for common AI workflows

Writing with real competitive context

"Claude, write a 1500-word article on remote team productivity tools. Before outlining, check what's ranking in the top 5 for that query and what angles competitors are using."

Claude calls google_search, reads the results, identifies the dominant content formats, and produces an outline that doesn't duplicate what already exists.

Code that searches before it generates

"Cursor, write a Python script to track our keyword rankings daily. Check what data Google Search returns for 'Python SEO tools' first."

Cursor calls the tool, inspects the actual JSON schema, and generates code against the real response structure — not a guessed one.

Market research in conversation

"What are the top Google Shopping results for 'standing desk under $500' right now? Extract price ranges and dominant merchants."

The assistant calls google_shopping, returns structured product data, and generates an analysis — no browser tab, no copy-paste, no manual research.

Get started

Generate an API key at app.autom.dev, add it to your MCP client config, and your AI assistant has live web access in under two minutes.

Full MCP setup documentation at docs.autom.dev/mcp. Full endpoint reference at docs.autom.dev.