Miinta Pipedrive MCP
Read-only MCP server for Pipedrive that provides a digest-ready sales queue with overdue follow-ups, due today activities, and deals missing next action.
README
Miinta Pipedrive MCP v0.1
Read-only MCP (Model Context Protocol) server for Pipedrive that provides a digest-ready sales queue with:
- Overdue follow-up activities (from a Pipedrive filter)
- Due today follow-up activities (from a Pipedrive filter)
- Deals missing next action (from a Pipedrive filter)
Features
- Single Tool:
miinta.sales_queue.get- Returns a comprehensive sales digest - Pipedrive API v2: Uses modern Pipedrive API for activities, deals, persons, organizations, and stages
- Data Enrichment:
- Stage names for deals
- Contact information (email) for persons
- Direct deal URLs with configurable company domain
- Days overdue calculation for activities
- Deal metadata (undone activities count, last mail times)
- Smart Features:
- Cursor-based pagination support
- Bulk fetching for persons/organizations (avoids N+1 queries)
- Caching for improved performance (configurable TTL)
- Configurable timezone support (default: Europe/Madrid)
- Standard MCP stdio transport for streaming communication
- Bearer authentication for Pipedrive API
- Flexible: Filter IDs provided per request (not hardcoded in environment)
Quick Start
git clone https://github.com/alvarolloret/pipedrive-mcp.git && cd pipedrive-mcpmake all- Add MCP with one command (pick one):
claude mcp add pipedrive -- docker run -i --rm --env-file /Users/alvarodemuller/miinta/pipedrive-mcp/.env pipedrive-mcporcodex mcp add pipedrive -- docker run -i --rm --env-file /Users/alvarodemuller/miinta/pipedrive-mcp/.env pipedrive-mcp
Configuration
Pipedrive API Token
Get your API token from Pipedrive:
- Go to your Pipedrive account settings
- Navigate to Personal preferences → API
- Copy your API token
Create a .env file based on .env.example:
# Required: Pipedrive API Token (Bearer token)
PIPEDRIVE_API_TOKEN=your_pipedrive_api_token_here
# Optional: Pipedrive API Base URL (default: https://api.pipedrive.com/v2)
PIPEDRIVE_API_BASE=https://api.pipedrive.com/v2
# Optional: Pipedrive Company Domain for deal URLs (default: app.pipedrive.com)
# Example: yourcompany.pipedrive.com
PIPEDRIVE_COMPANY_DOMAIN=yourcompany.pipedrive.com
# Optional: MCP Authentication Token (required if not localhost)
MCP_AUTH_TOKEN=your_mcp_auth_token_here
# Optional: Default timezone (default: Europe/Madrid)
DEFAULT_TIMEZONE=Europe/Madrid
# Optional: Cache TTL in seconds (default: 3600)
CACHE_TTL_SECONDS=3600
# Optional: Maximum items per section (default: 50)
MAX_ITEMS_PER_SECTION=50
# Optional: Transport mode - stdio, http, or sse (default: stdio)
TRANSPORT_MODE=stdio
# Optional: HTTP Server Port (default: 3000, only used if TRANSPORT_MODE is http or sse)
HTTP_PORT=3000
Required Pipedrive Filters
You need to create three saved filters in your Pipedrive account:
- Overdue Activities Filter: Filter for activities with
done=falseanddue_date < today - Today's Activities Filter: Filter for activities with
done=falseanddue_date = today - Deals Missing Next Action Filter: Filter for open deals without a next activity scheduled
Get the filter IDs from the Pipedrive UI or API. These will be provided when calling the tool (not in environment variables).
MCP Tool
The server exposes one tool: miinta.sales_queue.get
Input Schema (JSON Schema):
{
"type": "object",
"additionalProperties": false,
"properties": {
"filters": {
"type": "object",
"additionalProperties": false,
"properties": {
"overdue_activities_filter_id": { "type": "integer" },
"today_activities_filter_id": { "type": "integer" },
"missing_next_action_deals_filter_id": { "type": "integer" }
},
"required": [
"overdue_activities_filter_id",
"today_activities_filter_id",
"missing_next_action_deals_filter_id"
]
},
"limits": {
"type": "object",
"additionalProperties": false,
"properties": {
"overdue": { "type": "integer", "minimum": 1, "maximum": 200, "default": 25 },
"today": { "type": "integer", "minimum": 1, "maximum": 200, "default": 25 },
"missing": { "type": "integer", "minimum": 1, "maximum": 200, "default": 25 }
}
},
"timezone": { "type": "string", "default": "Europe/Madrid" },
"now": {
"type": "string",
"description": "Optional ISO datetime override for deterministic testing"
},
"include_people_orgs": { "type": "boolean", "default": true }
},
"required": ["filters"]
}
Example Request:
{
"filters": {
"overdue_activities_filter_id": 111,
"today_activities_filter_id": 222,
"missing_next_action_deals_filter_id": 333
},
"limits": {
"overdue": 25,
"today": 25,
"missing": 25
},
"timezone": "Europe/Madrid",
"include_people_orgs": true
}
Example Response:
{
"generated_at": "2026-02-16T07:45:00+01:00",
"timezone": "Europe/Madrid",
"sections": {
"overdue": [
{
"activity_id": 123,
"activity_subject": "Follow-up email",
"activity_type": "email",
"due_date": "2026-02-14",
"days_overdue": 2,
"deal": {
"deal_id": 456,
"title": "Escola X — Pilot",
"stage_id": 3,
"stage_name": "Conversation open neutral",
"url": "https://yourcompany.pipedrive.com/deal/456"
},
"person": {
"id": 10,
"name": "Maria Rius",
"email": "maria@example.com"
},
"org": {
"id": 20,
"name": "Escola X"
}
}
],
"due_today": [],
"missing_next_action": [
{
"deal_id": 789,
"title": "Universitat Y — Training",
"stage_id": 2,
"stage_name": "Contact",
"owner_id": 1,
"undone_activities_count": 0,
"next_activity_id": null,
"last_outgoing_mail_time": "2026-02-10T09:12:00Z",
"last_incoming_mail_time": null,
"url": "https://yourcompany.pipedrive.com/deal/789",
"person": {
"id": 11,
"name": "Joan Garcia"
},
"org": {
"id": 21,
"name": "Universitat Y"
}
}
]
},
"stats": {
"overdue_count": 1,
"due_today_count": 0,
"missing_next_action_count": 1
},
"source": {
"filter_ids": {
"overdue_activities_filter_id": 111,
"today_activities_filter_id": 222,
"missing_next_action_deals_filter_id": 333
}
}
}
Architecture
- Pipedrive Client (
pipedrive-client.ts): Handles all Pipedrive API v2 interactions- Activities filtering with
done=false, sorting - Deals filtering with
status=open,include_fields - Cursor-based pagination
- Bulk fetching for persons/organizations
- Activities filtering with
- Cache (
cache.ts): In-memory caching with TTL support - Sales Queue Service (
sales-queue.ts): Business logic for aggregating and enriching data- Stage name resolution
- Days overdue calculation
- Person/org enrichment with bulk fetching
- Deal URL generation with company domain
- MCP Server (
index.ts): MCP protocol implementation with stdio transport
API Version
This server uses Pipedrive API v2 endpoints:
/v2/activities- withfilter_id,done=false,sort_by=due_date/v2/deals- withfilter_id,status=open,include_fields/v2/persons- with bulkidsparameter/v2/organizations- with bulkidsparameter/v2/stages- cached for stage name resolution
Error Handling
The server implements two layers of error handling as per MCP spec:
- Protocol errors (JSON-RPC) for invalid args/unknown tool
- Tool execution errors: returns
isError: truewith readable error messages for:- Pipedrive auth failures
- Rate limit / transient upstream errors
- Invalid filter_id / permission issues
- Missing required parameters
Development
# Build TypeScript only
make build
# Full rebuild (clean + Docker image)
make all
# Clean compiled output
make clean
Acceptance Criteria
The server is considered "production-ready" when it:
- ✅ Accepts three filter IDs via tool call parameters
- ✅ Returns correct items from each filter
- ✅ Calculates correct
days_overduein specified timezone - ✅ Resolves correct
stage_namevalues from cached stage map - ✅ Includes person/org names when available
- ✅ Generates valid deal links using
PIPEDRIVE_COMPANY_DOMAIN - ✅ Handles pagination via
cursoruntil requested limits are filled - ✅ Includes deal fields:
undone_activities_count,last_incoming_mail_time,last_outgoing_mail_time - ✅ Uses bulk fetching for persons/orgs to avoid N+1 queries
- ✅ Returns output in exact spec format with
sections,stats,source
License
ISC
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
Neon Database
MCP server for interacting with Neon Management API and databases
E2B
Using MCP to run code via e2b.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.