Organizations need deeper context about their data — not just for human data practitioners but also for AI agents and LLMs. Without this understanding, data practitioners make flawed assumptions, AI agents produce misleading recommendations, and LLMs generate insights that appear superficially accurate but lack the nuanced depth necessary for sound business decision-making.

Collate’s managed OpenMetadata service helps provide that understanding by serving as the central nervous system for your data. Powered by its Unified Knowledge Graph, Collate creates a single source of truth across all your data systems. Today, we're extending this foundation with enterprise-grade support for the Model Context Protocol (MCP) built natively into the platform.

This integration means any user — technical or non-technical — can easily interact with your metadata through natural language conversations via systems such as ChatGPT or Claude. The use cases unlocked are compelling: not only can data teams easily read data for introspection and analysis, they can also write changes back — to build quality tests in bulk, create custom pipeline reports, generate glossary terms, and more — all in natural language.

Collate Delivers Deep Data Context Across Your Entire Organization

Collate has emerged as a leading Unified Metadata Platform for AI-powered data discovery, observability, and governance, creating a comprehensive, centralized view of organizational metadata. That’s critical because it’s essential to collect metadata from the entirety of your data landscape, from databases and dashboards, to pipelines and machine learning models, even API services.

Traditional data catalogs and vendor tools often do not support many of these additional sources, which results in lost context for downstream users and AI. Collate solves this problem with 90+ pre-built connectors that make it easy to ingest metadata from these data sources without relying on custom development for SDK or API integrations.

At its core, Collate is built on a schema-first, API-driven architecture, where a collection of JSON schemas defines a standard metadata model. This model is automatically translated into APIs and client code in multiple languages. With these building blocks, Collate organizes all the ingested metadata — technical, operational, and business — into a Unified Knowledge Graph. This graph maps all the relationships between data assets and the meaning behind them.

Think of it this way: If the schema maps the function of a neuron, the unified knowledge graph connects all the neurons together into a map of the central nervous system that shows how all the data interrelates.

The result is complete data context unified across the organization, breaking down silos. For example, a single UI view can show a dataset’s schema, owners, usage stats, upstream pipelines, downstream dashboards, quality tests, and even related ML models — all linked together. Data discovery, quality, lineage, observability, and governance are all interconnected on one platform. With our new enterprise-grade Model Context Protocol support, this organized, comprehensive metadata can be used not only by human data practitioners through the Collate UI, but also by AI agents and LLMs.

What Is Model Context Protocol?

The Model Context Protocol (MCP) is an emerging open standard (spearheaded by Anthropic and embraced by many industry leaders) that helps AI systems interact with external tools and data in a uniform, secure way. MCP works as a “universal translator” between AI assistants (or any LLM-driven application) and the myriad of systems where data and knowledge reside. Instead of building one-off integrations or brittle scripts for each data source, MCP provides a common interface.

In technical terms, MCP lets systems expose their capabilities — the data they hold and the actions they can perform — in a machine-readable schema that AI models can understand. For example, through MCP a data platform could advertise tools (functions an AI can call, like lookup_customer_by_email), resources (datasets or knowledge bases an AI can query), or even prompt templates that guide interactions. An AI assistant connected via MCP can then securely retrieve information or trigger actions by invoking these standardized functions, with proper authorization.

For organizations, MCP promises to bridge the gap between powerful AI reasoning and real-world data context. With a single, consistent protocol, an AI assistant can maintain awareness of business-specific context as it moves between different tools and datasets. Just as HTTP standardized how clients talk to servers, MCP is standardizing how AI models connect with data sources. It’s a simpler, more scalable way to give AI access to the knowledge it needs to produce relevant, accurate results.

Extending Collate with MCP: Context for Data Models, for Everyone

With the release of MCP support, Collate unlocks a new level of value from its unified knowledge graph. By embedding an MCP server directly into Collate, the platform can now expose rich metadata context to AI assistants and other MCP clients in real time. This means an AI tool like ChatGPT or Claude can query Collate to ask such questions as, “What is the definition of this metric?”, “Show me the lineage of data feeding this dashboard”, or “Who is the owner of this dataset and when was it last updated?” — and get answers based on live organizational metadata.

All the relationships and context captured in Collate’s graph become available to augment AI-driven analyses and automations. What makes this particularly powerful is that Collate's implementation of MCP is enterprise-ready by design.

The MCP server natively runs as part of the Collate platform, with no extra components to deploy. It isn’t just a thin wrapper over APIs — it taps into the platform’s unified knowledge graph to interact with its understanding of structured metadata and the network of relationships — while keeping only the details necessary to prevent LLMs from bloating their limited context windows.

This native integration brings multiple benefits:

Seamless Security and Governance:

The MCP interface inherits Collate’s robust security framework. Authentication and authorization are handled using the same role-based access controls (RBACs) and policy engine that governs the Collate UI and API. In effect, every AI agent or user connecting through MCP is subject to the exact same permissions as a regular user. So, a data steward will see only the assets they’re allowed to see, and an automated agent using a bot account can perform only the actions its role permits.

This tight integration with roles and policies enables consistent enforcement of governance rules. These controls also manage access to read data from Collate, but also access to write changes back, such as to generate descriptions, create a data-quality test, or update a glossary term. Under the hood, the MCP server can be secured with either bot tokens or Personal Access Tokens (PAT), just like the rest of the Collate ecosystem.

No Silos, No Glue Code

Since the MCP server is built into Collate, it works out-of-the-box with all the metadata in your platform. There’s no need to maintain separate integrations or custom code for AI assistants to talk to each data system — they just talk to Collate’s MCP endpoint and Collate provides the context. This is not a generic API proxy; it leverages the Unified Knowledge Graph to present logical actions and context (for example, a tool to fetch lineage for an asset, or to search glossary terms) rather than raw low-level endpoints.

That makes the interactions more powerful and semantically rich for AI. In short, MCP extends Collate’s mission from “a single place for all your data practitioners” to “a single place for all your data practitioners and AI applications” to access knowledge.

The Right Context for the Right Results

While Collate is designed as an API-first platform, APIs are defined for systems to communicate, and not all the information provided in their responses is helpful for LLMs. When designing Collate’s MCP implementation, we considered two main criteria:

1. The LLM should guide users through the metadata standard. For example, a Glossary Term should be the child of a Glossary or another Glossary Term. New users might not be aware of Collate’s data structure, so the LLM needs context to either follow its best judgment (if it finds related Glossaries when creating a Glossary Term) or prompt the user for further information to make the right decision (such as needing to create a Glossary first).

2. LLMs have limited context windows, and bloated contexts can lead the LLM to wrong answers. LLM context is a precious resource that requires the right amount of information, and no more. While scanning Collate’s Unified Knowledge Graph internally for the right piece of information, the MCP server returns only the necessary information to the LLM.

Enterprise-Grade Architecture

Using Collate’s existing infrastructure makes the MCP server inherently scalable and production-ready. Collate is built to handle millions of metadata entities and relationships in large enterprises, with a streamlined architecture that’s easy to deploy and scale securely.

The embedded MCP capability benefits from the same proven foundation — you don’t need a separate server cluster or special scaling strategy. This approach makes the combination of Collate and MCP a truly enterprise-grade solution for AI-enabled metadata management, combining open-source flexibility with the robustness that large organizations require.

Collate’s managed OpenMetadata Service natively includes the MCP Server, ensuring enterprise-grade security and governance without additional setup

The MCP server is backed by Collate’s Application Framework, which lets users add further functionalities to the platform. Installing the MCP application exposes the /mcp endpoint within the same server, allowing users in your organization to connect to it using their own Personal Access Token (PAT). Uninstalling the application at any point makes the MCP endpoint unavailable.

When the LLM wants to execute one of the provided tools, it takes the following steps:

1. Identify the user making the call, since the /mcp endpoint cannot be accessed without a JWT token.

2. Authorize the users that have sufficient permissions to perform the actions required by the tool, e.g., search certain metadata or update a specific asset.

3. Audit the sequence of actions that takes place.

4. Use the knowledge graph to retrieve the necessary information or perform the required updates.

Real-World Use Cases Unlocked by Collate + MCP

With Collate acting as an MCP server, a variety of powerful use cases emerge at the intersection of data intelligence and AI-driven automation.

Model and Data Lineage on Demand

Collate already captures end-to-end lineage: not just which tables feed which dashboards, but also which datasets were used to train an ML model, and how data flows through pipelines. With MCP, an AI assistant can trace these lineage links to answer questions or drive actions.

For example, if a model’s performance drops, an engineer could ask an AI, “What data sources and features does this model depend on?” and immediately get the lineage from model to source tables. In reverse, a governance AI might check, “Which reports or models will be impacted if Table X has a quality issue?” The MCP interface can fetch that impact analysis from the Unified Knowledge Graph. Contextual lineage available via AI means faster root cause analysis and impact assessment, with the AI doing the heavy lifting of traversing the graph.

Governance Automation and Compliance

Ensuring proper data use and compliance is a constant challenge. Collate’s integration with MCP enables intelligent agents to automate governance tasks. For instance, an AI agent can routinely scan the catalog for critical data assets missing owners or classifications, and then alert the right stewards or even assign the metadata if rules allow. It can also cross-check usage logs or model outputs against data sensitivity labels stored in Collate: “Alert me if any ML model is using fields tagged as Sensitive without approval.”

Since the AI agent can query metadata such as who owns an asset, its classification, and when it was last updated, it can enforce policies proactively. This reduces manual oversight by letting AI watch over the guardrails defined in metadata. As Collate captures schema changes, data quality test results, and access events, AI could also generate governance reports or playbooks. For example, it could summarize all data assets containing personal data and how they propagate, aiding in privacy compliance audits. The result is active metadata governance, with AI automatically ensuring policy enforcement and documentation keep up with the pace of data growth.

Collaboration via Conversational Interfaces

Perhaps MCP’s most transformative use case is how it allows everyone to tap into the metadata through natural language. Team members can get answers in the tools they already use — chat platforms, AI assistants, IDE plugins — all powered by the same centralized metadata knowledge.

Example Scenarios

• A data analyst can ask a team chatbot, “What’s the definition of the customer lifetime value metric, and which dashboard highlights it?” The bot (via MCP) can retrieve the glossary definition of that metric and find the dashboard that uses it, responding with a link.

• A data product manager could ask, “Which data sources feed into the weekly sales report?”, and get back a list of databases or pipelines from lineage information.

• A data engineer could quickly query via an IDE assistant to, “Show me any recent schema changes on the orders table”, which would pull the version history from Collate.

This conversational access to data context makes collaboration easier because everyone — technical or not — can get the information they need without digging through multiple systems. By integrating with messaging apps or notebooks through MCP, Collate becomes an intelligent data concierge for the organization. This helps break down silos and eliminate communication barriers: business users can explore the meaning of their data with the safety of governed answers, and technical users can save time by letting AI gather documentation and usage context.

The overall effect is a more data-informed culture, where knowledge flows freely but securely to those who need it, in plain language. As one community member put it, the future of data interaction is “conversational, not complicated”, and metadata helps make that possible.

Collate + MCP: The Central Nervous System of Your Data Stack

From Many Connections to One Unified Knowledge Graph: Reduce Complexity and Gain Complete Context

By combining Collate’s unified metadata platform with Model Context Protocol, organizations gain a powerful advantage: a central hub of context that connects all data, tools, people, and even AI agents in your data ecosystem. Collate was already the brain of the data stack — cataloging and relating everything. Now with MCP, it also becomes the nervous system, carrying critical information wherever it’s needed, instantly and intelligently.

Just as event streams (e.g. Kafka) became the central nervous system of modern data architectures by decoupling producers and consumers, MCP provides a standard, real-time interface for AI and tools to tap into enterprise knowledge.

Collate’s implementation ensures this is done in a governed, secure manner, suitable for the most stringent enterprise requirements:

• For data teams, this approach means less time spent writing glue code or hunting for information, and more time gaining insights and automating processes.

• For business stakeholders, it means quicker answers and more data-driven decisions, because the context finds you instead of the other way around.

• For the organization as a whole, Collate with MCP serves as an intelligent overlay on top of your data stack — a central nervous system where metadata flows freely but with control, enabling everything from smarter data discovery to automated governance actions.

Collate’s open-source core also means that this MCP integration is not a closed proprietary feature, but part of an open standard and community with OpenMetadata. Because it’s built on widely supported protocols (JSON schemas, APIs, and now MCP), MCP plugs into the rest of Collate's open ecosystem — including built-in RBACs, versioning, lineage engine, and more — to create an enterprise-grade solution for the AI era of data intelligence.

Where Metadata Transforms into Intelligence

MCP support brings new capabilities to Collate's powerful unified metadata platform, adding real-time AI integration that delivers context wherever it's needed. Whether it’s driving an AI assistant that chats with your data, automating compliance checks, or helping troubleshoot a broken data pipeline, Collate’s approach to MCP emphasizes open standards, enterprise-readiness, and broad accessibility.

The wide variety of use cases opened up by these new capabilities further positions Collate as the central nervous system of the modern data stack. Always aware, context-rich, and immediately actionable, Collate helps every data stakeholder (human or machine) make better, faster, and safer decisions with comprehensive metadata available through every interface.

Ready to get started? Check out our YouTube playlist of MCP use cases to see it in action, sign up for Collate Free Tier of our managed OpenMetadata Service, or visit the Product Sandbox to try out Collate with demo data.