Pipedrive Activities MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Pipedrive Activities as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Pipedrive Activities. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Pipedrive Activities?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Pipedrive Activities MCP Server
Connect Pipedrive CRM to any AI agent — manage your entire sales pipeline without switching tabs.
LlamaIndex agents combine Pipedrive Activities tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Deals — Search, create, and update deals with pipeline tracking
- Contacts — Find and create persons with email, phone, and organization
- Organizations — Search companies linked to deals and contacts
- Activities — Create calls, meetings, tasks, and emails
- Notes — Attach notes to deals, persons, or organizations
- Pipelines — View all pipeline stages and deal flow
The Pipedrive Activities MCP Server exposes 8 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Pipedrive Activities to LlamaIndex via MCP
Follow these steps to integrate the Pipedrive Activities MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 8 tools from Pipedrive Activities
Why Use LlamaIndex with the Pipedrive Activities MCP Server
LlamaIndex provides unique advantages when paired with Pipedrive Activities through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Pipedrive Activities tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Pipedrive Activities tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Pipedrive Activities, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Pipedrive Activities tools were called, what data was returned, and how it influenced the final answer
Pipedrive Activities + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Pipedrive Activities MCP Server delivers measurable value.
Hybrid search: combine Pipedrive Activities real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Pipedrive Activities to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Pipedrive Activities for fresh data
Analytical workflows: chain Pipedrive Activities queries with LlamaIndex's data connectors to build multi-source analytical reports
Pipedrive Activities MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Pipedrive Activities to LlamaIndex via MCP:
pd_activity_types
Default types: call, meeting, task, deadline, email, lunch. Teams can add custom types. Use to discover available activity types before creating activities, especially in accounts with custom configurations. List all activity types configured in Pipedrive — both default types (call, meeting) and custom types defined by the team
pd_create_activity
Subject and type are required. Type must be: call, meeting, email, task, lunch, or deadline (use pd_activity_types to see custom types). Set due_date (YYYY-MM-DD), due_time (HH:MM), and duration (HH:MM). Link to deals, persons, or orgs. Activities appear in the Pipedrive calendar and task queue for the assigned user. Schedule a sales activity in Pipedrive — a call, meeting, email follow-up, task, lunch, or deadline linked to deals or contacts
pd_deal_activities
Returns all scheduled, pending, and completed activities for that deal. Use when the user asks "what activities are on this deal?", "when is the next meeting for this deal?", or needs to review the engagement history of an opportunity. Get all activities (calls, meetings, tasks) linked to a specific deal for a complete activity history
pd_delete_activity
Consider marking as done (pd_mark_activity_done) instead to preserve history. Use only when the user explicitly wants to remove an activity from the record. Permanently delete a Pipedrive activity — this removes it from history and cannot be undone
pd_get_activity
Returns subject, type, dates/times, duration, notes, linked deal/person/org, and completion status. Use after listing activities to drill into a specific item. Get complete details of a specific Pipedrive activity by ID including notes, duration, and linked records
pd_list_activities
Returns subject, type (call/meeting/email/task/lunch/deadline), due date and time, whether it is done, and linked deal/person/org. Filter by done status: "true" for completed, "false" for pending/upcoming. Use when the user asks about tasks to do, scheduled meetings, overdue items, or recent sales activity. List Pipedrive activities (calls, meetings, tasks, emails) with due dates, types, and completion status
pd_mark_activity_done
The activity remains in history but is no longer in the pending/overdue queue. Use when the user says they completed a call, finished a meeting, or done with a task. Mark a Pipedrive activity as completed — removes it from the pending task queue and logs it as done
pd_update_activity
Only specified fields change. Use to reschedule (change due_date), rename (change subject), or reclassify (change type). Does not mark as done — use pd_mark_activity_done for that. Update an existing Pipedrive activity — reschedule, rename, or change type
Example Prompts for Pipedrive Activities in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Pipedrive Activities immediately.
"Search for deals with Acme Corp"
"Create a call activity for tomorrow at 2pm"
"Show me the pipeline stages"
Troubleshooting Pipedrive Activities MCP Server with LlamaIndex
Common issues when connecting Pipedrive Activities to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPipedrive Activities + LlamaIndex FAQ
Common questions about integrating Pipedrive Activities MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Pipedrive Activities with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Pipedrive Activities to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.