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name: pipedrive-mcp-connector description: "MCP connector for Pipedrive CRM – enables AI-powered sales pipeline management, deal tracking, and sales automation through Claude" license: Proprietary/API tags: ["business","crm","mcp","pipedrive","sales"] difficulty: intermediate time_to_master: "4-8 weeks" version: "1.0.0"

Pipedrive MCP Connector

Overview

This skill enables Claude to interact with Pipedrive through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Pipedrive's REST API, allowing natural language control of Pipedrive operations, intelligent automation, and AI-powered assistance for Pipedrive workflows.

When to Use This Skill

• Deal creation and pipeline management via AI
• Lead scoring and qualification
• Activity scheduling and follow-up reminders
• Sales report generation and forecasting
• Contact and organization management

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Pipedrive        │
│   (Client)   │◀────│   (TypeScript)  │◀────│   (REST API      )│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Pipedrive operations as tools Claude can invoke. The server translates natural language intentions into REST API calls.

Key Endpoints/Interfaces

/api/v1/deals, /api/v1/persons, /api/v1/organizations, /api/v1/activities, /api/v1/pipelines

Implementation

// Pipedrive MCP Server Implementation
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "pipedrive-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Pipedrive resources with optional filters",
  {
    query: z.string().optional().describe("Search query or filter"),
    limit: z.number().optional().describe("Max results to return"),
  },
  async ({ query, limit }) => {
    // Call Pipedrive REST API
    const response = await fetch(`${BASE_URL}/api/v1/deals`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Tool: Create Resource
server.tool(
  "create_resource",
  "Create a new resource in Pipedrive",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}/api/v1/deals`, {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${API_KEY}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ name, ...config }),
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: `Created: ${JSON.stringify(data)}` }],
    };
  }
);

// Tool: Analyze/Report
server.tool(
  "analyze",
  "AI-powered analysis of Pipedrive data",
  {
    type: z.string().describe("Analysis type"),
    timeframe: z.string().optional().describe("Time range for analysis"),
  },
  async ({ type, timeframe }) => {
    // Fetch data and provide AI analysis
    const response = await fetch(`${BASE_URL}/api/v1/deals`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Claude Desktop Configuration

{
  "mcpServers": {
    "pipedrive-mcp-connector": {
      "command": "node",
      "args": ["path/to/pipedrive-mcp-connector/index.js"],
      "env": {
        "PIPEDRIVE_API_KEY": "your-api-key",
        "PIPEDRIVE_BASE_URL": "https://your-instance-url"
      }
    }
  }
}

Best Practices

1. Authentication: Store API keys securely using environment variables; never hardcode credentials
2. Rate Limiting: Implement request throttling to respect Pipedrive API rate limits
3. Error Handling: Provide clear, actionable error messages for common failure scenarios
4. Pagination: Handle paginated responses for large datasets efficiently
5. Caching: Cache frequently accessed read-only data to reduce API calls
6. Security: Validate all inputs before passing to the Pipedrive API; sanitize outputs
7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Analyze the sales pipeline and identify deals at risk of stalling with suggested next actions"

Security Considerations

• All API credentials must be stored as environment variables
• Implement input validation and sanitization for all tool parameters
• Use HTTPS for all API communications
• Follow the principle of least privilege for API token permissions
• Audit log all write operations for compliance tracking

Resources

• Pipedrive Official Documentation
• MCP SDK Documentation: https://modelcontextprotocol.io
• MCP Server Examples: https://github.com/modelcontextprotocol/servers
• SkillGalaxy Repository: https://github.com/Sandeeprdy1729/skill_galaxy

Changelog

Version	Date	Changes
1.0.0	2026-04-01	Initial MCP connector skill

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