pipedrive · MCP Server

Improvado MCP — Pipedrive Data, Pipeline Clarity in Seconds

Improvado connects your Pipedrive deals, contacts, activities, and pipeline stages to any MCP-compatible AI agent. Ask about forecast accuracy, stage conversion, and rep performance in plain English. No exports, no dashboards, just answers.

46K+ metrics ·Read & Write access ·500+ platforms ·<60s setup

Read

Read: Pull Pipeline Data Without Opening Pipedrive

Query deals by stage, owner, close date, or custom field. Ask about win rates, average deal size, and activity coverage across any time period. Your AI agent gets a direct line to Pipedrive data through the MCP server.

Example prompts

"Show me all deals stuck in Proposal Sent for more than 14 days. Include deal value and last activity."

20 min → 30 sec

"What's the conversion rate from Demo Scheduled to Proposal Sent this quarter vs. last quarter?"

45 min → 1 min

"Which contacts haven't had any activity in 30 days but are tied to open deals over $20K?"

30 min → 45 sec

Works with Claude ChatGPT Cursor +5

Write

Write: Update CRM Records Through Conversation

Log activities, update deal stages, create follow-up tasks, and add notes — all without switching to Pipedrive. Your AI agent translates conversation context into CRM records.

Example prompts

"Log a 30-minute discovery call with this contact. Note that they asked about enterprise pricing and API access."

5 min → 20 sec

"Move all deals where the close date passed without activity to a Stalled stage and assign a follow-up task."

40 min → 1 min

"Create a deal for the new prospect we spoke to today. $45K value, 90-day close, assign to the enterprise pipeline."

8 min → 30 sec

Every action logged · Fully reversible · SOC 2 certified

Monitor

Monitor: Watch Your Pipeline Without Manual Check-Ins

Set your AI agent to flag deals going cold, pipeline coverage dropping below target, or activities falling behind SLA. Proactive CRM hygiene without the manual effort.

Example prompts

"Alert me if weekly new deal volume drops more than 20% versus the prior 4-week average."

Manual → auto

"Every Monday morning: show me this week's forecast — deals closing soon with probability above 70%."

1 hr → auto

"Flag any deal over $30K that has gone 7+ days without an activity log."

Manual → auto

Alerts sent to Slack, email, or your AI agent

Full cycle

The Closed Loop: Read → Decide → Write → Monitor

Your AI agent doesn't just surface data — it acts. Adjust pricing, update product descriptions, manage inventory, apply discounts — all through natural language. The MCP server translates intent into API operations.

Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.

Ideate

Launch

Measure

Analyze

Report

Iterate

One conversation. All six phases. Every platform.

The daily grind

Common problems. Direct answers.

Challenge 1

Pipeline Reviews Take Hours to Prepare

The problem

Preparing for a weekly pipeline review means exporting Pipedrive data, building a spreadsheet, categorizing deals by health, and formatting it for the call. By the time it's ready, the data is already a day old and something has changed.

How MCP solves it

Ask your AI agent for a real-time pipeline summary five minutes before the call. It pulls current stage distribution, deal health scores, forecast accuracy, and highlights at-risk opportunities — all from live Pipedrive data. No export, no formatting.

Try asking

Give me a pipeline review summary: deals by stage, this week's forecast vs. quota, and top 3 at-risk deals with reasons.

Answer in seconds

All data sources, one query

Challenge 2

Activity Coverage Gaps Are Invisible Until It's Too Late

The problem

Deals go cold without anyone noticing because there's no single view showing which opportunities haven't had contact. By the time a rep flags it in a review meeting, the window to re-engage has passed. Pipedrive has the data, but no one monitors it systematically.

How MCP solves it

Set a recurring alert for deals exceeding your activity SLA. Your AI agent checks Pipedrive daily, identifies gaps, and can even draft re-engagement emails for deals at risk. The monitoring that used to require a dedicated ops person now runs automatically.

Try asking

Which open deals in the enterprise pipeline had no activity in the last 10 days? Include deal owner and last contact date.

Full detail preserved

No data loss on export

Challenge 3

Custom Field Data Never Surfaces in Reporting

The problem

Your team captures rich data in Pipedrive custom fields — competitor names, use case, decision timeline, budget range. But this data lives in individual records. No standard Pipedrive report aggregates it, so the insights are effectively buried.

How MCP solves it

The MCP server makes custom field data queryable just like standard fields. Ask your AI agent to analyze win rates by competitor, average deal size by use case, or close rate by budget range. Structured data that was previously invisible becomes immediately useful.

Try asking

What's our win rate when the primary competitor is listed as Vendor X? Compare to deals where no competitor is recorded.

Unified data model

Compare anything side by side

👥 Teams

One Framework. Five Roles. Zero Setup.

Same MCP connection, different workflows for every team member. Each role asks in natural language — the MCP server handles the complexity (rate limits, auth, schema normalization, governance) behind the scenes.

Agency CEO

Portfolio health. Client risk. Revenue signals.

Media Strategist

70% strategy, not 70% ops. Auto campaign QA.

Marketing Analyst

Zero wrangling. Cross-platform. AI narratives.

Account Manager

QBR decks auto-generated. Call prep in 30s.

Creative Director

Performance-to-brief. Predict winners before spend.

FAQ

Common questions

What Pipedrive data can AI agents access through this MCP?

Deals (all stages, custom fields, values, close dates), contacts and organizations, activity logs (calls, emails, meetings, tasks), pipeline configurations, users and teams, and deal history. All standard and custom fields are queryable in natural language.

Can I query across multiple Pipedrive pipelines at once?

Yes. The MCP server has access to all pipelines your API token can reach. You can query across pipelines simultaneously or filter to a specific one. Cross-pipeline comparisons — like win rate by pipeline type — work in a single prompt — all through Improvado's hosted MCP server.

Can AI agents write back to Pipedrive, or is it read-only?

Both read and write are supported. Write operations include creating and updating deals, logging activities, adding notes, updating contact records, and managing tasks. You control scope through Pipedrive's API token permissions. Most teams start with read-only and expand as they get comfortable.

How is this different from Pipedrive's built-in reporting?

Pipedrive's native reports are fixed templates. The MCP connection lets you ask any question in plain English — including multi-step analyses, custom field aggregations, and comparisons that native dashboards can't produce. You're not limited to the charts Pipedrive pre-built — all through Improvado's hosted MCP server.

Does this work with Pipedrive's email and calling features?

Yes. Emails sent through Pipedrive's connected inbox, calls logged through Pipedrive calling, and activities synced from calendar integrations are all accessible. Your AI agent can surface the full communication history for any deal or contact.

How long does setup take?

Under 5 minutes. Generate a Pipedrive API token from your personal settings, connect it to Improvado, then add one line to your MCP client config. All your deal and contact data is accessible immediately — no pipeline configuration needed.

Stop Reporting. Start Executing.

Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.

SOC 2 Type II GDPR 500+ Platforms