Data Management Glossary
Model Context Protocol (MCP)
What is the Model Context Protocol?
The Model Context Protocol (MCP) is an emerging open standard that enables AI models and agents to securely connect with external tools, data sources, and systems in a consistent way. Instead of building one-off integrations, MCP provides a framework for interoperability, allowing agentic AI systems to dynamically pull in context, take actions, and access enterprise resources.
Why is MCP important for Agentic AI?
Agentic AI refers to AI systems that can plan, reason, and act autonomously to achieve goals. For these agents to be useful in enterprise environments, they must safely access real-time business data, applications, and workflows. MCP makes this possible by providing a standardized way to extend AI capabilities, enforce security controls, and ensure reliable connections across different ecosystems.
What is the role of Unstructured Data Management & Komprise?
Since up to 80% of enterprise data is unstructured (files, images, genomics data, sensor logs, etc.), agentic AI needs a way to find, classify, and deliver the right unstructured data to its reasoning process. This is where Komprise plays a key role. By analyzing, tiering, and mobilizing petabyte-scale unstructured data without vendor lock-in, Komprise ensures MCP-connected AI agents can access the most relevant, accurate, and policy-compliant data for training, inference, and decision-making, while controlling costs and reducing risks such as exposing sensitive information.
How Komprise Universal File MCP Connects AI Agents to Unstructured Data
Most MCP servers are built for one application or one storage system. An enterprise with NAS, object, and cloud storage from several vendors ends up running a stack of connectors, and each one loads its own tool definitions into the agent context window before a single file is read. That overhead is MCP bloat.
Komprise Universal File MCP replaces that stack with one MCP interface to file and object data across every storage silo. It is built on the Komprise Global Metadatabase, which applies a consistent schema to every file regardless of where it lives. Agents query metadata first and load full files only when needed, and Deep Analytics filters out irrelevant, outdated, conflicting, and duplicate files before they reach the model. Every query runs under the requesting user’s existing access permissions, so a response never includes data that user is not authorized to see. Agents also query tiered and archived data directly, with no rehydration.
Model Context Protocol (MCP) FAQs
What is Model Context Protocol (MCP)?
Model Context Protocol is an open standard that defines how AI models and agents connect to external tools, data sources, and systems. An MCP server exposes a set of tools and resources, and any MCP-compatible AI client calls them in a consistent way. That means developers build one integration instead of a custom connector for every model and every system.
Who created Model Context Protocol?
Anthropic introduced MCP as an open standard in November 2024. It has since been adopted across AI clients, developer tools, and enterprise software vendors as a common way to give agents governed access to tools and data.
Source: Anthropic
What is the difference between MCP and an API?
An API is an interface to one specific system, and each one has its own authentication, calls, and data formats. MCP is a standard layer that sits above APIs so an AI agent discovers and calls tools the same way everywhere. An MCP server often wraps one or more APIs, then presents them to the agent in a form the model understands.
Why is MCP important for AI agents working with unstructured data?
Unstructured data accounts for 80% to 90% of enterprise data, and it is spread across NAS, object, and cloud storage from multiple vendors. An agent that only reaches structured systems misses most of the context it needs. MCP gives agents a standard way to search and retrieve files, but the quality of the answer depends on whether the MCP server indexes all storage, filters out noise, and enforces file permissions.
Source: IDC, https://www.idc.com/
What is MCP bloat?
MCP bloat is the token and context overhead created when an agent loads many MCP servers, each with its own tool definitions, before it does any work. Storage-specific connectors multiply the problem in enterprises that run several storage platforms.
How do you give AI agents secure access to enterprise file data through MCP?
Use an MCP server that enforces the existing file and object permissions of the requesting user on every query, rather than a service account with broad read access. The server also needs to exclude sensitive data such as PII from responses where policy requires it, and keep an audit trail of what agents accessed.
How does Komprise support MCP for unstructured data?
Komprise Universal File MCP gives AI agents one MCP interface to file and object data across NAS, cloud, and object storage, built on the Komprise Global Metadatabase. It returns curated, permission-aware results, loads metadata before full files to cut token use, and reaches tiered and archived data with no rehydration. Komprise improves AI accuracy by filtering poor-quality data.
Which layer of the AI data platform does MCP belong to?
MCP operates at layer 5, AI Delivery and Consumption, where agents and applications consume data. Its results are only as good as layers 2 through 4 beneath it: Metadata and Discovery, Classification and Governance, and Enrichment and Curation. Komprise operates at layers 2 through 4 and exposes the result to agents through Universal File MCP.
See the AI Data Platform glossary page for the full five-layer model.