Enterprise AI solutions have come a long way in solving the connectivity problem. By allowing Large Language Models (LLMs) to tap into scattered data stores, AI can now reach the information it needs. But simply connecting to data doesn't mean an AI model actually has a comprehensive understanding of the data or data landscape or that it can judge the depth and data quality required for reliable decision-making. This disconnect creates three major hurdles for effective agentic workflows:

• The Knowledge Gap: AI models often fail to realize that different labels, naming conventions, or IDs across separate tools actually point to the exact same real-world entity.
• The Trust Gap: When faced with conflicting information from multiple places, standard models lack a built-in way to figure out which source is truly authoritative, high-quality, and up-to-date.
• The Action Gap: AI agents might know the steps required to execute a task, but without governance controls, they can't verify whether the underlying data is compliant, accurate, or even permitted for that specific use case.

Here, we'll cover how to bridge these gaps seamlessly:

Catalog and Data Discovery Skill: The skill provides instructions and reusable workflows to Claude on how to perform specific metadata-only discovery from Informatica's Cloud Data Governance and Catalog service. Additionally it informs Claude on how to inspect data assets – tables, columns, glossary terms, ownership, certification, and PII/sensitivity tags – thus verifying that the asset is fit for purpose, before handoff to the data discovery and execution tool. It also ensures that LLM adheres to strict anti-hallucination guardrails, treating missing metadata as an explicit gap rather than guessing. Once a trusted asset is identified, it passes verified information to downstream data exploration tools.

Connectors: These components handle secure communication, linking Claude directly to Informatica's MCP endpoints (including the Catalog Discovery MCP and the CLAIRE Data Exploration Agent MCP) via Informatica’s Secure MCP Gateway. For the Informatica Claude connectors now available in Claude directory, Refer to Informatica Catalog Discovery and Informatica Data Exploration now.

Informatica Catalog Discovery MCP: Serving as the primary gateway to Cloud Data Governance and Catalog (CDGC), this MCP gives AI agents the ability to navigate and inspect enterprise data assets easily. Using natural language queries or specific keywords, AI agents can surface rich metadata, explore data dependencies, and ground every answer in a governed enterprise information.

Informatica CLAIRE Data Exploration Agent MCP: Designed as a specialized interface for CLAIRE's data exploration features, this MCP grants AI agents direct access to dataset and attribute telemetry inside IDMC. Sitting downstream from orchestration, it lets systems with concrete analysis goals interact directly with underlying data assets safely and efficiently.

Follow this step-by-step walkthrough to see the plugin at work:

1. Set up OAuth 2.1 authorization credentials so connector components can connect directly with MCP servers hosted on IDMC.
2. Configure the Informatica connectors for Informatica Catalog Discovery MCP and Informatica CLAIRE Data Exploration Agent MCP.
3. Configure the Informatica Plugin.
4. Enable the plugin within Claude.
5. Execute your prompt (for instance, "run quarterly sales EMEA in Q2") and review the output alongside the reasoning on how it arrived at the result.

1. Login into the Informatica IDMC Console specific to your pod.
2. Choose AI Agent Engineering for Services pop up menu.

9. Paste Claude callback url (e.g. https://claude.ai/api/mcp/auth_callback).
10. Click Add.
11. Click Next.

Enable the plugin within Claude

1. In the Claude Desktop click on +New from the navigation pane on the left.

Execute your prompt

Imagine an executive or business analyst tasked with building a Q2 performance evaluation. Although this seems like a straightforward request, carrying it out without grounded metadata knowledge, authoritative definitions, cataloged assets, and governance safeguards creates major risks for inaccuracies. For example, the company might define Q2 as running from May to July, track regional metrics across three distinct fields instead of one, and maintain several revenue fields where only a single column holds certified figures.

Now, we will demonstrate how the Informatica Plugin for Claude Platform empowers agentic workflows to utilize catalog-based intelligence. By grounding actions in governed metadata, the AI avoids making blind assumptions about naming conventions, verifies business terms against authoritative standards, traces entity relationships, and executes requests within validated compliance boundaries.

Although this process successfully fetched precise local revenue metrics for the timeframe, it further offers full visibility into its reasoning on how it derived the financial totals while avoiding common operational missteps.

Summary

The Informatica Plugin for Claude Platform addresses key enterprise AI challenges by integrating Claude Enterprise directly with Informatica IDMC. By connecting with Cloud Data Governance and Catalog (CDGC) and CLAIRE Data Exploration Agent MCP servers, it equips AI workflows with rich metadata context, authoritative data sources, and robust governance guardrails, ensuring business tasks are executed with accuracy, compliance, and trust.

To learn more about how Informatica and Claude Ground your agents in your trusted enterprise data, read this post: From Model Intelligence to Enterprise Intelligence: How Informatica + Claude Ground AI in Trusted Data.