Connect Pipedrive to ChatGPT, Claude, Gemini, and other LLMs

Description

Pipedrive is a sales CRM that stores deals, contacts, organizations, and activities. To analyze that data in ChatGPT, Claude, Gemini, or another LLM, use scheduled Coupler.io imports, which load one entity at a time into a queryable dataset.

Also searched as Pipedrive CRM.

How to connect

Connect Pipedrive to your AI tool with Coupler.io. Pick the tool you use to see how to install it.

Coupler.ioCoupler.io · Data platform · Third-partyRecommendedRead-onlyBest for: Analysing sales history at length or alongside other systems

Coupler.io

Coupler.io imports one Pipedrive entity per data flow on a schedule and exposes the result to Claude through its own MCP server, so Claude queries a stored dataset rather than calling Pipedrive during the conversation.

Works with

Pick the AI tool you use. The install steps below change to match it.

ClaudeAnthropic · Remote MCPChatGPTOpenAI · Remote MCPGemini CLIGoogle · Remote MCPGemini EnterpriseGoogle Cloud · Remote MCPMicrosoft Copilot StudioMicrosoft · Remote MCPPerplexityLocal MCPCursorRemote MCPOpenClawRemote MCPCustom MCPRemote MCP

• Each AI tool sees only the datasets from data flows that have that tool as a destination. To use the same data in several tools, add each one as a destination on the data flow.
• Every AI tool destination replaces the data on each run; append mode is not supported.

How to install

Works in Claude web, Claude desktop, Claude mobile, Cowork, Claude Code.

In Coupler.io

1. In Coupler.io, create a data flow and add Pipedrive as the source, then choose the entity you want.
2. Narrow the export with a Pipedrive filter ID, a last-modified date range, or a column selection if you do not need every field.
3. Choose Claude as the destination in the data flow's destination step.
4. Give the data flow a clear name, because Claude refers to the dataset by that name.
5. Run the data flow manually once and wait for it to finish successfully.
6. Set a refresh schedule once that first run has succeeded.

In Claude web, desktop, and Cowork

1. Install the Coupler.io connector from Claude's connector directory and click Connect.
2. In the Connectors menu, click Connect again and sign in to Coupler.io.
3. Open a new chat. Claude asks permission to use the Coupler.io tools the first time; allow them.

In Claude Code

1. Start a new Claude Code session.
2. Paste the connection command shown in the Claude destination step of your data flow.
3. Restart the session.
4. Run /mcp, select Coupler.io, and click Authorize in the browser window that opens.

Check it worked

Ask Claude to list the datasets it can see; the Pipedrive data flow should appear under the name you gave it. Then compare the row count Claude reports with the run in Coupler.io, because the preview alone does not confirm what Claude received.

Good to know

• On individual Claude Free and Pro plans the official connector may connect but fail to load its tools. The documented workaround is a custom connector: in Coupler.io, open AI integrations, click Custom connector, and copy the URL. Remove the official connector in Claude, then go to Connectors, Add, Add custom connector, give it a name with plain letters and spaces only (for example Coupler Custom), paste the URL, and connect.
• Local use with Claude desktop is also available through a Desktop Extension.

Coupler.io guide for ClaudeCoupler.io in the Claude connectors directory

Connects through the Coupler.io ChatGPT app.

In Coupler.io

1. In Coupler.io, create a data flow and add Pipedrive as the source, then choose the entity you want.
2. Narrow the export with a Pipedrive filter ID, a last-modified date range, or a column selection if you do not need every field.
3. Choose ChatGPT as the destination in the data flow's destination step.
4. Give the data flow a clear name, because ChatGPT refers to the dataset by that name.
5. Run the data flow manually once and wait for it to finish successfully.
6. Set a refresh schedule once that first run has succeeded.

In ChatGPT

1. Open the Coupler.io app in ChatGPT, from the link in your Coupler.io account or the apps section of ChatGPT settings.
2. Install the app and authorize it with your Coupler.io account.
3. Start a new chat to pick up fresh data after a run, or ask ChatGPT to fetch it again in an ongoing conversation.

Check it worked

Ask ChatGPT to list the datasets it can see; the Pipedrive data flow should appear under the name you gave it. Then compare the row count ChatGPT reports with the run in Coupler.io, because the preview alone does not confirm what ChatGPT received.

Coupler.io guide for ChatGPT

In Coupler.io

1. In Coupler.io, create a data flow and add Pipedrive as the source, then choose the entity you want.
2. Narrow the export with a Pipedrive filter ID, a last-modified date range, or a column selection if you do not need every field.
3. Choose Gemini CLI as the destination in the data flow's destination step.
4. Give the data flow a clear name, because Gemini CLI refers to the dataset by that name.
5. Run the data flow manually once and wait for it to finish successfully.
6. Set a refresh schedule once that first run has succeeded.

In Gemini CLI

1. In the Gemini CLI destination step, generate the command that adds the Coupler.io MCP server, then run it in your terminal. It looks like gemini mcp add coupler --transport=http --scope=user https://mcp.coupler.io/mcp/....
2. Start Gemini CLI and run /mcp auth coupler to sign in to Coupler.io.

Check it worked

Ask Gemini CLI to list the datasets it can see; the Pipedrive data flow should appear under the name you gave it. Then compare the row count Gemini CLI reports with the run in Coupler.io, because the preview alone does not confirm what Gemini CLI received.

Coupler.io guide for Gemini CLI

Added as a custom MCP data store with OAuth credentials from Coupler.io.

In Coupler.io

1. In Coupler.io, create a data flow and add Pipedrive as the source, then choose the entity you want.
2. Narrow the export with a Pipedrive filter ID, a last-modified date range, or a column selection if you do not need every field.
3. Choose Gemini Enterprise as the destination in the data flow's destination step.
4. Give the data flow a clear name, because Gemini Enterprise refers to the dataset by that name.
5. Run the data flow manually once and wait for it to finish successfully.
6. Set a refresh schedule once that first run has succeeded.

In Gemini Enterprise

1. In the Gemini Enterprise destination step, create OAuth credentials and copy the Client ID and Client Secret. The secret is shown only once; if you lose it, regenerate it.
2. In Gemini Enterprise, go to Data stores, Create data store, and choose Custom MCP server.
3. Paste the MCP Server URL, Authorization URL, Token URL, and the scope mcp from the destination step, together with the Client ID and Client Secret.

Check it worked

Ask Gemini Enterprise to list the datasets it can see; the Pipedrive data flow should appear under the name you gave it. Then compare the row count Gemini Enterprise reports with the run in Coupler.io, because the preview alone does not confirm what Gemini Enterprise received.

Coupler.io guide for Gemini EnterpriseGoogle Cloud documentation

Added to a Copilot Studio agent as an MCP tool.

In Coupler.io

1. In Coupler.io, create a data flow and add Pipedrive as the source, then choose the entity you want.
2. Narrow the export with a Pipedrive filter ID, a last-modified date range, or a column selection if you do not need every field.
3. Choose Microsoft Copilot Studio as the destination in the data flow's destination step.
4. Give the data flow a clear name, because your Copilot Studio agent refers to the dataset by that name.
5. Run the data flow manually once and wait for it to finish successfully.
6. Set a refresh schedule once that first run has succeeded.

In Microsoft Copilot Studio

1. Open your agent in Copilot Studio and confirm generative orchestration is on.
2. Go to Tools, Add a tool, New tool, Model Context Protocol, and paste the Coupler.io MCP server URL shown in the destination step.
3. For authentication, choose OAuth 2.0 and Dynamic discovery, then click Create. No client ID or secret is needed.
4. The first time someone uses the tool, they see a consent card in chat and sign in to Coupler.io once.

Check it worked

Ask your Copilot Studio agent to list the datasets it can see; the Pipedrive data flow should appear under the name you gave it. Then compare the row count your Copilot Studio agent reports with the run in Coupler.io, because the preview alone does not confirm what your Copilot Studio agent received.

Coupler.io guide for Microsoft Copilot StudioMicrosoft documentation

Perplexity currently uses the local Coupler.io MCP server, which runs in Docker.

In Coupler.io

1. In Coupler.io, create a data flow and add Pipedrive as the source, then choose the entity you want.
2. Narrow the export with a Pipedrive filter ID, a last-modified date range, or a column selection if you do not need every field.
3. Choose Perplexity as the destination in the data flow's destination step.
4. Give the data flow a clear name, because Perplexity refers to the dataset by that name.
5. Run the data flow manually once and wait for it to finish successfully.
6. Set a refresh schedule once that first run has succeeded.

In Perplexity

1. In Perplexity, go to Settings, Connectors, click Add new connector, and choose the simpler settings.
2. Name the connection coupler-io and enter the command docker run --pull=always -e COUPLER_ACCESS_TOKEN --rm -i ghcr.io/railsware/coupler-io-mcp-server.
3. Add an environment variable called COUPLER_ACCESS_TOKEN, then generate a Coupler.io personal access token and paste it as the value.
4. Save the configuration and accept the security warning about Docker.

Check it worked

Ask Perplexity to list the datasets it can see; the Pipedrive data flow should appear under the name you gave it. Then compare the row count Perplexity reports with the run in Coupler.io, because the preview alone does not confirm what Perplexity received.

Coupler.io guide for Perplexity

In Coupler.io

1. In Coupler.io, create a data flow and add Pipedrive as the source, then choose the entity you want.
2. Narrow the export with a Pipedrive filter ID, a last-modified date range, or a column selection if you do not need every field.
3. Choose Cursor as the destination in the data flow's destination step.
4. Give the data flow a clear name, because Cursor refers to the dataset by that name.
5. Run the data flow manually once and wait for it to finish successfully.
6. Set a refresh schedule once that first run has succeeded.

In Cursor

1. Open the Coupler.io entry in Cursor's MCP directory and click Add to Cursor.
2. In Cursor, find the new Coupler.io integration, click Needs authentication, then sign in with your Coupler.io account and grant access.
3. Open the AI pane and ask about your dataset.

Check it worked

Ask Cursor to list the datasets it can see; the Pipedrive data flow should appear under the name you gave it. Then compare the row count Cursor reports with the run in Coupler.io, because the preview alone does not confirm what Cursor received.

Coupler.io guide for CursorCoupler.io in the Cursor MCP directory

Connects to the Coupler.io MCP server through mcporter.

In Coupler.io

1. In Coupler.io, create a data flow and add Pipedrive as the source, then choose the entity you want.
2. Narrow the export with a Pipedrive filter ID, a last-modified date range, or a column selection if you do not need every field.
3. Choose OpenClaw as the destination in the data flow's destination step.
4. Give the data flow a clear name, because OpenClaw refers to the dataset by that name.
5. Run the data flow manually once and wait for it to finish successfully.
6. Set a refresh schedule once that first run has succeeded.

In OpenClaw

1. Install the coupler-io skill from ClawHub. It holds the commands that connect the Coupler.io MCP server to OpenClaw with mcporter.

Check it worked

Ask OpenClaw to list the datasets it can see; the Pipedrive data flow should appear under the name you gave it. Then compare the row count OpenClaw reports with the run in Coupler.io, because the preview alone does not confirm what OpenClaw received.

Coupler.io guide for OpenClawCoupler.io in ClawHub

Connects any MCP-compatible client with a personal access token.

In Coupler.io

1. In Coupler.io, create a data flow and add Pipedrive as the source, then choose the entity you want.
2. Narrow the export with a Pipedrive filter ID, a last-modified date range, or a column selection if you do not need every field.
3. Choose Custom MCP as the destination in the data flow's destination step.
4. Give the data flow a clear name, because your MCP client refers to the dataset by that name.
5. Run the data flow manually once and wait for it to finish successfully.
6. Set a refresh schedule once that first run has succeeded.

In your MCP client

1. In the Custom MCP destination step, click Generate token and copy it together with the MCP server URL.
2. Add the token and URL to your MCP client's JSON config, following the example config shown there.

Check it worked

Ask your MCP client to list the datasets it can see; the Pipedrive data flow should appear under the name you gave it. Then compare the row count your MCP client reports with the run in Coupler.io, because the preview alone does not confirm what your MCP client received.

Good to know

• Treat the personal access token as a secret and do not share it.
• Behind a corporate firewall, allow outbound connections to the Coupler.io MCP endpoint.

Coupler.io guide for Custom MCP

Overview

What Coupler.io gives you from Pipedrive

• Entities: Deals, Persons, Organizations, Activities, Files, Leads, Call logs, Products
• A ninth entity, All deals (BETA), is named in troubleshooting and best practices but is not listed in either entity table
• Per-entity options: a Pipedrive filter ID, a last-modified date range, and column selection
• No Pipedrive reports: in-app analytics and forecast reports are not available through the API, so this is raw CRM data only

An example question you can ask

Which deals have sat in the same stage longest, and what are they worth in total by owner?

How the data reaches the AI tool

Coupler.io reads the Pipedrive API, stores the result as a dataset, and serves it to Claude over its MCP server. Queries run on Coupler.io's side, so a large dataset does not have to fit into the model's context. Claude sees only datasets from data flows that have Claude as their destination.

Historical data

Not documented (no retention or backfill window is stated; the only date control documented is a last-modified filter, which can be left empty to export every record)

Refresh

• A successful manual run is required before a schedule can be set
• Scheduled refresh is described as hourly, daily, or a custom interval
• The platform supports intervals from every 15 minutes to monthly, depending on plan

Combining several sources

• One data flow can take several sources and combine them with Join or Append before the data reaches Claude
• Each data flow exports one entity, so combining Deals with Persons means a flow for each and a join
• Documented join keys: Deals to Persons on person_id.value, and Deals to Organizations on org_id.value

Prerequisites

• A Pipedrive account with access to the data you want to export
• The All deals (BETA) entity requires Pipedrive global admin access
• A Coupler.io account

Limits to expect

• Pipedrive rate limits are 20 requests per 2 seconds on entry-level plans; Coupler.io paces requests automatically for large accounts, which lengthens run times
• An import that does not finish within 30 minutes times out
• Call logs are fetched at a maximum of 50 records per page, a Pipedrive API constraint
• The Activities entity is fetched with user_id=0, so it returns activities from all users rather than only the connected account
• The Pipedrive API returns active records only; deleted or archived deals, persons, and organizations are excluded
• Date range filtering applies to Deals; for other entities it may have limited or no effect
• Row counts in Claude can differ from the Coupler.io preview when an identifier repeats, so the count should be checked in Claude

Related skills

A skill gives the AI tool instructions for a task. It connects no data, so connect Pipedrive first.

• Ai citation to revenue funnel

  • Which of our pages do ChatGPT, Perplexity or Gemini cite
  • Does our AI search visibility bring traffic, signups or sales
  Marketing & Ads+ 9 more sourcesView skill

• Sales analytics

  • How is the pipeline
  • Win rate by segment
  Sales+ 4 more sourcesView skill

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Questions

Can Claude update my CRM, or only read it?

Only read it. Coupler.io is read-only against Pipedrive: Claude queries an imported dataset and cannot create or change records.

Can I get my Pipedrive reports and forecasts into Claude?

Not as reports. Pipedrive's in-app analytics and forecast reports are not available through the API, so Coupler.io exports raw CRM data only and you would rebuild the analysis in Claude.

Will an import include deals I have deleted or archived?

No. The Pipedrive API returns active records only, so deleted and archived deals, persons, and organizations are excluded from a Coupler.io import.

Does the activity data cover my whole team?

Yes, and that may surprise you. Coupler.io fetches the Activities entity with user_id=0, which returns activities from all Pipedrive users rather than only the connected account. Separately, some Pipedrive plan levels restrict visibility of other users' activities.

Why is my import slow or timing out?

Pipedrive rate limits entry-level plans to 20 requests per 2 seconds, and Coupler.io paces its requests automatically for large accounts, which lengthens runs. An import that does not finish within 30 minutes times out. Narrowing the export with a filter ID, a last-modified date range, or fewer columns is the documented way to keep runs short.

The connector shows as connected but Claude cannot see my data. Why?

On individual Claude Free and Pro plans there is a documented defect where the official Coupler.io connector connects but its tools never load, so Claude cannot query the datasets. Coupler.io documents a custom connector URL as a workaround and reports that it works on all plans.

Sources and corrections

Researched on 2026-10-04. One route is covered, researched on 4 October 2026: the Coupler.io data platform. Its documentation was read in full, covering the data available, prerequisites, access, and limits.

Something here wrong or out of date? Open a correction issue and it will be re-checked against the source.

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