Enterprise knowledge inventory
Map repositories, documentation, dashboards, ownership signals, policies, services, and data products into a usable operating layer.
Enterprise MCP servers
Governed AI agents need more than prompts. They need enterprise context.
Narona Data builds enterprise MCP servers that turn repositories, warehouse metadata, business definitions, verified query paths, and governance rules into an operating layer for Claude, Codex, IDE agents, and internal AI assistants.
Why this exists
An enterprise MCP server is the context layer for agentic AI work.
Most enterprise AI pilots fail because agents lack the operating context humans use every day: which system owns a metric, which table contains a signal, which repository implements a workflow, which data is sensitive, and which query path has already been verified.
A governed MCP layer makes that context available through structured tools and resources. Agents can discover the right domain, inspect the right system, retrieve the right metadata, and answer business questions without forcing users through Slack routing, meetings, or manual repository spelunking.
Capabilities
What Narona builds into an enterprise MCP implementation
Data estate metadata map
Structure tables, columns, lineage, query history, definitions, and petabyte-scale warehouse context so agents can reason across the data estate.
Progressive MCP tool disclosure
Expose broad discovery tools first, then reveal narrower domain, system, table, column, and query tools only when relevant.
AI agent governance
Design access controls, audit paths, evaluation loops, and role-aware retrieval so agentic work scales safely inside the enterprise.
Proof
Concrete healthcare payer results, not category theory
In a large healthcare payer environment, Narona Data used this architecture to support 300+ people across 80–90 teams with faster, cheaper, governed answers from frontier models.
View the interactive MCP architecture timeline- 100+ repositories and hundreds of thousands of files structured for agent use
- 10,000 business questions turned into an evaluation corpus
- Verified query paths tied to real business questions
- Roughly 70.8x reduction in thinking-token usage without sacrificing accuracy
- 10–20x faster question turnaround compared with skills/plugins alternatives
- 300+ users across 80–90 healthcare payer teams served in under one minute
FAQ
Enterprise MCP server questions
What is an enterprise MCP server?
An enterprise MCP server uses the Model Context Protocol to expose governed company systems, code, metadata, business definitions, and approved tools to AI agents through a structured access layer.
Why use MCP instead of a chatbot over documents?
A chatbot over documents usually retrieves text. An enterprise MCP server can expose tools, resources, query paths, ownership context, data lineage, and governance controls that agents can use to complete work.
How does progressive tool disclosure help?
Progressive disclosure reduces tool noise by revealing narrower tools only after the agent has enough context to choose them. That can reduce reasoning overhead, lower cost, and improve reliability.
Can MCP support healthcare data governance?
Yes. MCP servers can be deployed with role-aware access, auditability, approval boundaries, and domain-specific policies so agents retrieve governed context instead of unrestricted sensitive data.