Cognism's MCP connects LLMs such as Claude and ChatGPT directly to Cognism's verified, GDPR-first company and contact data, using the Model Context Protocol (MCP), an open standard for linking AI assistants to external data. Once in the Claude directory, connect it under Settings, then Connectors, then search, count, or pull verified contacts and companies straight into a workflow, alongside tools like Salesforce, HubSpot, or Gong.
If you've searched for Cognism MCP, you're probably trying to work out one thing: does it actually exist, and what does it do?
Good news: it does exist, and you'll need an existing Cognism account to use it. Cognism's MCP connects LLMs such as Claude and ChatGPT directly to Cognism's verified company and contact data.
Until now, that data has lived inside the Cognism platform, or wherever you've built an integration for it. More and more revenue teams are doing real work inside Claude and ChatGPT day to day, and data that stays locked outside those tools becomes a gap you have to work around. This connector bridges that gap.
This article covers what the MCP is, how to connect it, what you can do with it today, and how it differs from using the Cognism API directly. If you want the full technical picture of how MCP itself works, that's covered separately below.
Cognism's MCP lets Claude and ChatGPT talk to Cognism's data through the Model Context Protocol (MCP), an open standard that lets AI assistants connect to external tools and data sources. Instead of copying data in and out, the AI assistant can ask Cognism's data directly, inside the same conversation you're already having.
Cognism's version of this is controlled and read-only. You're not exporting bulk records or enabling unrestricted access – you're giving Claude or ChatGPT a controlled way to search and check Cognism's verified company and contact data, on request, within the access your organisation already has.
If you want the full picture of how MCP works as a protocol, read ‘MCP servers defined: what they are and how they work’, which covers that in depth.
Setting it up doesn't need a developer. To connect to the Cognism MCP, add it as a custom connector in your LLM of choice, using the URL available inside the Cognism platform.
If you’re using Claude or ChatGPT, it'll appear directly in the connector directories, making it straightforward to connect.
But before then, note that this works on any LLM, personal or company account, as long as it's a paid plan – free accounts don't support custom connectors.
It’s also worth knowing if you're on a Microsoft stack that Cognism's MCP already supports Microsoft Copilot Studio as a custom connector. It's just not listed in Copilot's own connector directory yet.
In practice, you can ask your AI assistant to:
Every answer draws on Cognism's verified, GDPR-first data, and stays within the access controls your organisation already has. So, your AI assistant only ever sees and returns what your team is entitled to. Not sure where to start? Read our list of 20 tasks GTM teams can do with Cognism's MCP.
Most revenue teams aren't working in one tool at a time. A typical Claude conversation might already pull in Salesforce, HubSpot, Gong, or Outreach – Cognism's MCP sits alongside those, in the same thread, rather than asking you to switch between separate tools to get an answer.
One detail worth noting is that Claude narrates its reasoning as it works, rather than just handing back a result. You can see what data it pulled from Cognism and why, before you act on anything it returns. That matters even more in an agentic workflow, as you want to be able to check the AI's workings and not blindly trust the output.
The Cognism API is for teams building their own applications, automations, or integrations, for example, into a data warehouse or a CRM. It needs API access on your subscription, a generated token, configured entitlements, and someone technical to build and maintain the integration.
With the Cognism MCP, there’s nothing to build on the Claude or ChatGPT side. You’ll need an existing Cognism account, with the appropriate access and account settings configured so the MCP returns the relevant, governed data for your team. Once that’s in place, simply connect Cognism through your AI assistant’s settings and start using it. For custom applications or integrations, using an API remains the right approach.
More teams are shifting day-to-day work into AI assistants than they were even a year ago, and AI adoption in sales is already the norm. 87% of sales organisations now use some form of AI for tasks like prospecting, forecasting, lead scoring, or drafting emails, according to Salesforce's State of Sales report. When Cognism's data is available in those tools, it means one less tool your team has to check before acting on the data.
You don't need a large team or a technical build to benefit. A smaller RevOps team, or someone effectively running RevOps on their own, can get Cognism's data to surface in the assistant they're already using, with no engineering lift required.
Cognism's MCP is available for Cognism customers with Standard or Pro subscriptions. If you want to try it, look for the MCP tab in your platform settings, or get in touch with your account team for support. Explore Cognism's MCP today.