Enterprise MCP Server for Healthcare Payer Operations

The company had the knowledge. The organization could not reach it fast enough.

A large healthcare payer had the raw material for an enterprise AI operating model: more than 100 repositories, hundreds of thousands of files, tens of thousands of tables, hundreds of thousands of columns, and roughly petabyte-scale data context.

The problem was not that knowledge was missing. The problem was that every answer required routing through people, teams, Slack threads, tickets, repository searches, dashboards, and tribal memory. That slowed operational questions, made AI adoption harder, and kept valuable data assets hidden inside local team boundaries.

Narona Data started by turning real business usage into the evaluation substrate for the system. We analyzed patterns of data usage across the business and used them to create an initial dataset of 10,000 business questions. Those questions became the benchmark for whether agents could retrieve the right context, generate the right query path, and answer without wasting time or tokens.

What had to become navigable

• 100+ code repositories and hundreds of thousands of files
• Tens of thousands of tables and hundreds of thousands of columns
• Business definitions, ownership signals, dashboards, policies, and operational workflows
• Healthcare payer data governance constraints and role-aware access boundaries
• Real patterns of data usage across teams
• An initial corpus of 10,000 business questions for AI agent evaluation and system tuning

The business requirement

The system had to make company knowledge available to people and agents in under one minute, without turning the context layer into another expensive chatbot, brittle plugin collection, or manually maintained skills library. That made enterprise MCP server architecture the right fit: structured enough for governance, flexible enough for Claude, Codex, and future agents, and measurable enough to improve over time.

Enterprise MCP server for healthcare operations

The platform organized company knowledge by business domain, department, team, system, repository, service, data product, table, column, and concept. This gave agents a structured map of the company rather than a flat search index.

The enterprise MCP server exposed governed tools and resources for application code, analytics engineering, infrastructure, warehouse metadata, dashboards, policies, and business definitions. Claude, Codex, IDE agents, and internal assistants could use the same context layer instead of each team building its own plugin, skill pack, or prompt library.

Progressive tool disclosure for lower agent cost

A key design choice was progressive disclosure of MCP tool calls. Instead of exposing agents to a huge undifferentiated tool surface, the MCP server revealed the next useful tool only when the agent had enough context to choose it. Agents could start from a business question, narrow into a domain, inspect the relevant systems, then reveal repository, schema, table, column, and query tools as needed.

That made the system materially more efficient. Agents spent fewer reasoning steps deciding what to call, carried less irrelevant context, and avoided the failure mode where a massive tool catalog becomes noise.

What the MCP layer exposed

• Repository maps across application code, analytics engineering, infrastructure, and documentation
• Table and column metadata across a petabyte-scale payer data estate
• Progressive tool-call disclosure by domain, system, data product, table, and concept
• Verified query paths tied to real business questions
• Links between code, data models, dashboards, policies, and business definitions
• Retrieval endpoints designed for Claude, Codex, IDE agents, and internal agent workflows

Design principle

The system was built as company infrastructure. Every response had to be grounded in traceable assets, respect healthcare data governance and role-aware boundaries, and return quickly enough to keep the user inside the workflow. The goal was not search. The goal was operational context that agents could use to do work.

10,000 verified business questions as an evaluation layer

Once the MCP server was live, teams could ask operational questions directly through Claude, Codex, IDE agents, and internal assistants instead of starting from repository spelunking or human routing.

The evaluation system was as important as the interface. The initial 10,000-question corpus created a baseline for accuracy, latency, query quality, and cost. Narona Data verified the queries needed to answer those questions, then used the results to build a self-learning system: as usage grew, the platform could identify weak spots, missing concepts, ambiguous terms, and under-documented data assets.

This turned enterprise AI adoption into a compounding loop. The more teams used the MCP layer, the better the organization understood which questions mattered, which datasets were underused, and where agents needed better context.

How this supported AI agent governance

• The system evaluated answers against verified query paths, not only fluent text
• Usage patterns showed which business questions were recurring, ambiguous, or high value
• Tool-call traces exposed where agents needed more context or narrower tool surfaces
• Access controls and auditability made the same layer usable in healthcare payer workflows
• Improvements could be prioritized from real enterprise usage instead of one-off prompt feedback

What changed for users

• 300+ people across 80–90 teams could get grounded answers in under one minute
• Teams could ask in operational language instead of knowing the right repository, schema, or owner first
• Agent workflows started with governed context instead of blank-prompt exploration
• The system accelerated AI adoption by giving enterprise users a safe, useful default path
• Human and agentic work became easier to coordinate across teams and departments

Adoption effect

The MCP server became shared operating context. It gave the enterprise a way to scale agentic work without asking every team to build its own skills, plugins, semantic layer, or local context pack.

Progressive disclosure made the system faster, cheaper, and more accurate.

The most important result was not only answer speed. It was answer speed with accuracy and cost control. Compared with alternatives based on manually built skills, plugin collections, and less-structured tool access, the MCP service produced faster turnaround on business questions while using far less model reasoning.

Narona Data measured a roughly 70.8x reduction in thinking-token usage without sacrificing answer accuracy. In practice, that meant users saw much faster answer cycles, roughly 10–20x faster turnaround compared with skills/plugins-style alternatives, and much lower operating cost even when using frontier models.

The business value extended beyond question answering. Teams resolved operational questions with fewer handoffs, meetings, and manual investigations. The MCP layer also surfaced adjacent datasets, reusable logic, underused tables, and pricing signals that had been hidden by organizational boundaries.

That visibility created a pipeline of follow-on pricing-intelligence work in the healthcare payer business. Teams could find relevant data faster, connect it to operational decisions, and identify opportunities that were difficult to see from inside a single function.

Business result

• Company-wide answers in under one minute for 300+ users across 80–90 teams
• Roughly 70.8x reduction in thinking-token usage without sacrificing accuracy
• 10–20x faster turnaround on questions versus skills/plugins-style alternatives
• Much lower operating cost even with frontier models
• Shorter operational question cycles and lower coordination cost across teams and departments
• More efficient human workflows and agentic execution paths
• Faster AI adoption inside a large enterprise because agents could use governed, trusted context
• Multiple pricing-intelligence initiatives with aggregate savings in the millions to tens of millions

Enterprise MCP server FAQ

What is an enterprise MCP server?

An enterprise MCP server uses the Model Context Protocol to expose governed company context to AI agents through structured tools and resources. For this healthcare payer, the MCP layer connected code repositories, data estate metadata, business definitions, ownership signals, and verified query paths.

Why does progressive tool disclosure reduce AI agent cost?

Progressive disclosure keeps the model from seeing every possible tool at once. The agent starts with broad context, then receives narrower domain, system, table, column, or query tools only when they are relevant. That reduces tool noise, reasoning overhead, thinking-token usage, and operating cost.

How can MCP support healthcare data governance?

MCP can sit behind role-aware access controls, audit logs, security review, and domain-specific permissions. The agent receives governed context and approved tool surfaces instead of unrestricted access to every repository, table, or sensitive data asset.

How do you evaluate enterprise AI agents?

Narona Data evaluated the system with 10,000 business questions, verified query paths, answer-quality checks, latency measurements, tool-call traces, and cost metrics. The same evaluation loop also made the system self-learning by showing which concepts, tools, and datasets needed improvement.

The case for the agentic operating system became clear: when company knowledge is structured, governed, progressively exposed, and evaluated continuously, AI becomes an operating layer for finding, prioritizing, and executing business improvements.

Related proof points: AI-powered permissioning for healthcare data access governance, Snowflake data governance for healthcare, and AI-driven BI migration with self-service analytics agents.