MCP Integration: Why AI Agents Need Context to be Effective and Trustworthy

AI agents are rapidly gaining traction in organizations worldwide, providing an intuitive way for businesses to deliver the trustworthy data everyone needs to drive better decision-making. But as AI agents become more sophisticated and are used more pervasively across the organization, their effectiveness increasingly hinges on having the right business context. Whether responding to a customer inquiry, generating reports, or automating internal workflows, AI agents—and the large language models (LLMs) that power them—need ready access to accurate, timely, and relevant enterprise data.

What is Model Context Protocol (MCP)?

Created by Anthropic, MCP is “an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools.” The protocol offers developers a safe and portable way to connect enterprise data to AI applications like Claude Desktop, Cursor, and ChatGPT.

Instead of maintaining bespoke integrations, MCP enables data teams to access a standard LLM integration they can use across multiple sources and consumers, making it easier to scale. Think of it as glue that binds enterprise systems and AI applications, providing the tooling needed to accelerate AI adoption. 

Why MCP is Important for Agentic AI

Delivering the trustworthy data business users need is a challenge for many organizations. Despite establishing large data engineering teams and procuring a myriad of data-related technologies such as traditional master data management (MDM), data governance, data catalogs, and data integration, many business users are still asking basic questions such as “who are our customers?”

Data teams, despite their best efforts, remain slow and inefficient. They spend much of their time on data cleaning, yet are often unable to keep pace with the deluge of data flowing in. This is where AI agents can help.

When it comes to using agentic AI for data mastering, AI agents can solve difficult challenges around curation, record matching, and preventing data degradation, and they can automate many of the routine tasks that consume much of a data team’s time. AI agents are also being used more broadly by business users across the organization, but their success depends on three critical elements:

1. Quality: AI agents need accurate, complete, trustworthy data to avoid hallucinating a bunch of garbage that damages your business. And when the results the agents deliver are misleading or downright wrong, users need an easy way to provide feedback to correct them.
2. Context: Retrieval-augmented generation (RAG) and knowledge graphs enable users to ask sophisticated questions and receive immediate, actionable responses. But to deliver accurate responses, AI agents require business context that comes from enterprise data. 
3. Partnership: For AI agents to be successful, it’s essential that humans remain in-the-loop, providing valuable feedback and insight to ensure the agents are delivering insights business users can trust. 

To ensure business users can easily consume clean, mastered data via consumption endpoints like Claude Desktop, it’s imperative that organizations connect LLMs with clean, mastered data. MCP is emerging as the leading method for doing so—and Tamr is leaning in.

How AI-Native MDM Provides the Context AI Agents Need

MCP offers rapid and exciting advances in the evolution of agentic AI by enabling organizations to connect their enterprise data with LLMs. However, while MCP simplifies the integration of enterprise data with LLMs, data cleansing remains critical. After all, bad data can easily be as harmful as any hallucination.

Tamr is disrupting traditional MDM by giving business users a clean, mastered view of critical business entities, at the right level of abstraction, so they can consume it in AI applications and use it to drive the business forward. For example, when Tamr’s AI-native MDM is connected to AI agents, business users can quickly and easily query enterprise data using LLMs such as Claude or ChatGPT. And because these agents have context from Tamr, users gain access to a trustworthy, 360-degree view of the data that’s easy to consume.

Consider this scenario, also shown in the following video:

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A salesperson returns from a conference and wants to follow up with a contact he met at the booth. He wants to find out what data his company has on the contact so he can personalize his follow-up. He opens up Claude and asks a simple question: “What do we know about Robert Sully?”

Without MCP integration with his company’s instance of Tamr, Claude’s response would be vague and generic, delivering basic information that may be incorrect, outdated, incomplete, or lacking context. However, because the AI agent (Claude) is integrated with Tamr, the agent has the context needed to provide the salesperson with accurate information based on clean, mastered data in Tamr. First and foremost, Robert’s last name is Scully, not Sully. Tamr knows this because it has semantic search built in. By matching the salesperson’s inquiry against key business entity data, Tamr recognizes that the two names are the same person. Tamr also surfaces additional information, including Robert’s company name, title, address, and phone number.

The salesperson notices that Robert Scully lives in Chattanooga, Tennessee, a city that he plans to visit next month. He would like to invite Robert for coffee, but he doesn’t know which coffee shops are close to Robert’s office. He asks Claude a follow-up question: “What coffee shops are located near Robert’s office?” Claude is also integrated with Google Mmaps via MCP integration, so it can take the address information from Tamr and coffee shop information from Google and provide the salesperson with a list of coffee shops nearby.

The salesperson selects a coffee shop and then asks Claude to draft an email to Robert Scully, inviting him to coffee. Because of Claude’s LLM integration with critical business systems, the email includes relevant information from the company’s source systems, adding further personalization to the follow-up.

How Tamr is Disrupting MDM with MCP Integration for LLMs

Unlike other vendors in the market that are only focused on data query, data cataloging, and data movement, Tamr is taking a different approach. Tamr starts with the needs of business users and improves the quality of data they need and use and by delivering it in a consumable way via AI agents. 

Tamr’s Virtual Chief Data Officer (vCDO) is a smart AI agent that enables organizations to expose mastered business entities to end users via a user-friendly, interactive chat interface. And when coupled with MCP integration, Tamr extends this vCDO capability even further, enabling business users to query and consume trustworthy enterprise data within a familiar chat interface. MCP integration provides peace of mind as well, giving organizations a secure, governed way to expose critical business entities, with access controls in place, via AI agents.

Using mastered entities within Tamr as the data for AI agents, organizations can prevent hallucinations, false responses, or misinformation. And by exposing mastered data from Tamr’s AI-native MDM solution, users can access the data via any number of consumption end points, interrogate it by asking complex questions, and provide feedback on the data to further improve its quality.

AI agents promise to change how organizations interact with and consume enterprise data. And the key to their success is having high-quality data, context, and a feedback mechanism to ensure data remains accurate and complete. Tamr’s AI-native MDM solution provides critical capabilities to help organizations harness the power of AI agents and deliver the clean, trustworthy data every business user needs to make better, more confident decisions. 

To learn more, watch our recorded webinar, Tamr and Google Agentspace: Better, Smarter Agents Reasoning with the Best Data Context.