AI agents are quickly becoming a new interface for interacting with data. Instead of writing scripts or manually calling APIs, developers can connect structured datasets directly to AI tools and query them through prompts.

PredictLeads supports this workflow through Model Context Protocol (MCP), which allows AI tools such as Cursor and Claude Desktop to access PredictLeads datasets directly.

This guide explains how to connect PredictLeads MCP and what capabilities it unlocks for developers, analysts, and AI-powered workflows.

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What PredictLeads MCP Enables

When connected through MCP, PredictLeads datasets become available directly inside AI development tools.

Instead of manually querying APIs, users can retrieve company intelligence through prompts.

For example, an AI agent can retrieve:

• company news events
• hiring signals from job openings
• technologies used by a company
• similar companies in a market

Once connected, the AI tool can query PredictLeads datasets automatically and use the information inside workflows, scripts, or research tasks.

This makes it possible to combine AI reasoning with structured company data.

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Step 1: Install Cursor

To begin, install Cursor, which provides the environment where MCP servers can be configured.

Note before we start: This guide shows how to do it directly via Claude Desktop. If you want to do it via Cursor instead, skip to the image showing where to “Open Cursor Settings → Tools & MCP and select Add Custom MCP.”
You can do that by opening Cursor and navigating to Settings, where you'll find Tools & MCP. From there, simply select Add Custom MCP. Easy peasy, lemon squeezy.

After installation, open the application and navigate to:

File → Settings

Then open the Developer settings and select Edit Config. This will open the configuration file where MCP servers are defined.

You will need Node.js installed locally for the MCP helper to work properly.

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Step 2: Configure the MCP Server

Next, open the Claude configuration file (claude_desktop_config).

This file defines which MCP servers Claude can access.

Inside the configuration, add the PredictLeads MCP server.

Example configuration:

{
  "mcpServers": {
    "PredictLeads": {
      "type": "http",
      "url": "https://mcp.predictleads.com/",
      "headers": {
        "X-Api-Key": "{your_api_key}",
        "X-Api-Token": "{your_api_token}"
      }
    }
  }
}

Replace {your_api_key} and {your_api_token} with your PredictLeads credentials.

These credentials can be found in the PredictLeads subscription dashboard.

Here you can add a custom MCP server and paste the PredictLeads configuration.

This step connects Cursor to the PredictLeads MCP endpoint.

Step 3: Save and Restart

After adding the configuration:

1. Save the configuration file
2. Close Claude Desktop and Cursor
3. Restart both applications

When the applications restart, PredictLeads should appear as an available MCP server. (Go back to the “claude_desktop_config” file)

Step 4: Start Querying PredictLeads Data

Once MCP is connected, Cursor can retrieve PredictLeads datasets directly.

For example, you can run prompts that query:

• Company News Events
• Job Openings
• Technologies used by a company
• Similar companies
• And more…

The AI assistant can retrieve this data and use it inside code generation, analysis workflows, or research tasks. Note – in the top right corner you can open the chat where you can start using PredictLeads MCP via promts)

What This Opens for Developers and AI Agents

Connecting PredictLeads through MCP unlocks several new workflows.

AI-Powered Market Research

Developers can ask AI agents to analyze markets and retrieve company signals such as funding events, hiring activity, or product launches.

AI Sales Intelligence

AI agents can retrieve technographic and hiring signals to identify companies that match specific sales criteria.

Automated Competitive Monitoring

Agents can monitor competitors and retrieve structured signals about hiring, technology adoption, or partnerships.

AI Developer Assistants with Company Data

Developers can build internal AI assistants that query PredictLeads datasets while writing code or exploring markets.

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PredictLeads as a Data Layer for AI Agents

PredictLeads datasets already power many workflows across:

• sales intelligence
• market research
• investment analysis
• competitive monitoring

With MCP integration, these datasets can now be accessed directly inside AI development environments.

Instead of manually building API integrations, developers can connect PredictLeads once and allow AI agents to retrieve company intelligence dynamically.

This makes PredictLeads a powerful data layer for AI-native workflows.

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Get Started with PredictLeads MCP

You can connect PredictLeads MCP to your AI development tools and start querying datasets directly from your AI assistant.

PredictLeads MCP allows AI agents to access:

• company news signals
• hiring data
• technographic datasets
• company relationships
• market intelligence signals

To learn more about PredictLeads datasets and APIs, visit: https://docs.predictleads.com/mcp_integration/introduction

Any questions? Do let us know by visiting the following “link“.

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