Native Model Context Protocol (MCP) from Pipedrive
A model context protocol, or MCP, is an open standard that allows large language models (LLMs) and AI assistants to securely interact with structured data and external services.
MCPs provide a standardized way for AI assistants to securely access business data, tools and APIs. This allows AI applications to retrieve information, update records and automate workflows across connected systems.
So, rather than building separate integrations for every AI tool, businesses can use MCP to make their data and functionality available to any MCP-compatible AI application through a single and consistent interface.
How does MCP work?
MCP acts as a bridge between an AI application and a business tool such as Pipedrive.
When a user asks an AI assistant a question or requests an action, the AI can use MCP to retrieve information from connected systems, perform approved actions on behalf of a user, combine data from multiple sources to complete a task or automate workflows across business applications.
For example, an AI assistant connected to Pipedrive through MCP can:
- Find details about a deal, contact or organization
- Create or update records
- Retrieve sales pipeline information
- Generate summaries of customer interactions
- Answer questions using data stored in Pipedrive
Why use Pipedrive’s native MCP?
Pipedrive’s native MCP makes it easier for AI assistants to securely and reliably connect with Pipedrive and interact with your CRM data directly from AI assistants.
By using a standardized protocol, MCP reduces the need for custom integrations and allows compatible AI tools to access information and perform actions in Pipedrive more efficiently.
This helps businesses adopt AI-powered workflows faster while maintaining a consistent and reliable connection between their tools and CRM data.
MCP access
MCP does not automatically grant access to your Pipedrive data.
Access is managed through authentication, permissions and authorization settings. AI applications can access information and perform actions only when explicitly permitted by the user or administrator.
This ensures that you remain in control of your Pipedrive data while benefiting from AI-powered capabilities.
Key use cases
Once connected, your AI assistant can access data, perform actions and help automate workflows in Pipedrive, based on the permissions granted to your account.
Use case | Example prompt |
Ask instead of click | Show me all open deals that haven’t been updated in two weeks |
| Let AI update your CRM | Create a follow-up call for next Monday and add today’s meeting notes to the deal |
| Get instant pipeline insights | Which deals are most likely to close this month, and what are the next steps? |
| Build from meeting notes | Here are my notes from today’s call. Create a deal, contact, and follow-up activity |
| Cross-tool automation | Summarize all overdue deals and draft follow-up emails for each one |
| Move deals through the pipeline | Move the [company name] deal to the [specific stage name] stage and schedule a follow-up for [date] |
| Create new leads instantly | Create a new lead for [company name]. Contact is [person name], [job title], [[email protected]] |
| Add contacts from a photo | Attach a photo of a business card and tell the assistant to add the person as a contact and link them to a specific deal |
| Find anything in seconds | Search deals, contacts, organizations, and leads by name, email, phone, and pull up complete records, history, and pipeline position on demand |
Set up guide
The setup process depends on the AI assistant your company uses.
Refer to the appropriate guide below for step-by-step instructions on connecting Pipedrive through MCP.
If you need the list of MCP tools, you can find it in the following entry:
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