MCP Architecture Guide for Government: Connecting Agency Systems to AI Agents

1. The Agency Data Problem

Government agencies run a broad set of systems that were never designed to talk to each other. HRIS platforms like Workday, SAP, and Oracle HCM hold employee records and org structure. Learning management systems like Cornerstone and SAP SuccessFactors track mandatory training and certifications. Case management systems handle citizen applications and status tracking. Citizen services portals manage public inquiries and program eligibility. Document management platforms like SharePoint hold SOPs, policies, and regulatory documents. Ticketing systems like ServiceNow handle IT incidents and provisioning. Identity systems like Okta and Azure AD — often with PIV/CAC card integration — manage roles, clearance levels, and entitlements.

Each system has its own API, its own auth model, its own schema, and its own limitations. Every time you build something that needs data from more than one system — a compliance dashboard, an onboarding workflow, an AI agent — you re-solve the integration problem from scratch.

The constraints in government make this harder. Data sovereignty is non-negotiable. FedRAMP authorization, NIST 800-53 controls, and FOIA requirements shape every architectural decision. Internal IT teams rightly prioritize security over integration work, which means cross-system projects move slowly or stall entirely.

Model Context Protocol (MCP) is an open standard that addresses this. You build one thin wrapper per system (an "MCP server"), add a broker that routes and governs requests, and then any application or agent can ask cross-system questions through a single, stable interface — without any agency data leaving the network perimeter.

This guide walks through the process step by step for government environments.

2. Inventory Your Agency Systems

Before writing any code, you need a clear picture of what systems the agency operates, what data each one holds, and what APIs are available. For each system, document the vendor, data types, API surface, authentication model, rate limits, and data sensitivity classification.

The goal is not to wrap every system on day one. Pick the two or three sources that matter most for your first use case — for example, HRIS and LMS for a compliance training dashboard — and start there. You can add more MCP servers later without changing any existing applications.

3. Build MCP Servers for Each System

An MCP server is a thin wrapper around one system's API. It exposes that system's capabilities as "tools" — structured operations with defined inputs, outputs, permissions, and audit behavior.

One server per system

Keep each MCP server focused on a single source system. An HRIS MCP server wraps HRIS API calls. An LMS MCP server wraps LMS API calls. This means when Workday ships a breaking API change, you update one server and nothing else breaks.

Define tool schemas

Each tool has a name, a description (so agents know when to use it), input parameters, and output shape. The tool name becomes the stable contract that applications depend on.

mcp_server.add_tool(
    name="get_employee_training_compliance",
    description="Check mandatory training completion status
                for an employee, including overdue items
                and upcoming deadlines",
    parameters=["employee_id", "training_category"],
    policy={
        "rbac": ["training_officer.read", "hr.read",
                 "manager.read_direct_reports"],
        "clearance_enforcement": True,
        "pii_minimization": True,
        "audit": True,
        "foia_classification": "routine"
    }
)

Notice the policy block: this tool requires the training_officer.read, hr.read, or manager.read_direct_reports role. Clearance enforcement ensures the requester's clearance level authorizes access to the employee's data. PII minimization strips unnecessary personal data from the response. Every call is audited and classified for FOIA purposes.

Security defaults for government

Every MCP server in a government deployment should ship with these defaults:

• FedRAMP-aligned controls — all infrastructure meets FedRAMP Moderate (or High) baseline requirements.
• NIST 800-53 compliance — access controls, audit logging, and incident response mapped to NIST control families.
• Air-gapped deployment option — for classified or sensitive environments, MCP servers can run entirely disconnected from external networks.
• Clearance-level enforcement — the server checks the requester's clearance level before returning data about personnel with higher clearance.
• FOIA-ready audit logs — complete records of who accessed what data, when, and why. Configurable retention per records management schedule.
• System-scoped service accounts — each server authenticates with its own credentials, scoped to only the operations it needs.
• Deny-by-default tool exposure — tools must be explicitly enabled. No open-ended API passthrough.
• Input validation + output filtering — validate parameters before calling the upstream API. Filter PII from responses.
• Request signing / mTLS — mutual TLS between the broker and servers so both sides verify identity.

All data stays within the agency network perimeter. No agency data is used for model training.

4. Add an MCP Broker

With individual MCP servers in place, the broker is the layer that ties them together. Applications and agents talk to the broker; the broker routes requests to the right MCP server, enforces policy, caches responses, and provides observability.

Clearance-level data boundaries

The broker enforces clearance-level data boundaries at the routing layer. Different roles see different data:

• Employees — can query their own training status, their own HR records, their own ticket history.
• Managers — can query training and performance data for direct reports only.
• HR and training officers — can query workforce-wide compliance data, aggregated or by region.
• Citizen-facing agents — can query case status and services data, but only information the citizen is authorized to see about their own case.

IL4/IL5 workload support

For agencies handling Controlled Unclassified Information (CUI) or sensitive workloads, the broker supports Impact Level 4 and IL5 environments. This means the entire MCP layer — broker, servers, and the AI agents connecting through them — can run in GovCloud or on-premises infrastructure rated for these workloads.

Audit and compliance

All requests through the broker are logged with full context: requester identity, clearance level, tool called, parameters sent, data returned, and timestamp. These logs are structured for Inspector General investigation and FOIA compliance. Retention policies align with the agency's records management schedule.

5. Connect Agents

Once the broker is running, any MCP-compatible agent can connect to it. The agent sees a list of available tools (from all connected MCP servers) and can call whichever tools it needs to answer a question.

This is where the architecture pays off. When a manager asks "How is this employee's mandatory training going?" the agent can:

1. Call get_employee_record on the HRIS MCP server to pull employee details, division, and role.
2. Call get_employee_training_compliance on the LMS MCP server to check mandatory training completion status.
3. Combine the results into a single, coherent answer that includes what is complete, what is overdue, and what is upcoming.

The agent does not need to know how Workday's REST API works, what Cornerstone's endpoint structure looks like, or how to authenticate to either system. It calls stable tool names, and the MCP servers handle the rest.

6. Example: Employee Compliance Training

A training officer at the agency asks:

"Which employees in Region 4 are overdue on mandatory cybersecurity training?"

The agent breaks this into two calls:

1. HRIS MCP server — get_employees_by_region("Region 4") returns all employees in Region 4: 143 employees across 3 offices.
2. LMS MCP server — get_training_compliance_by_employees(employee_ids, "cybersecurity") returns completion status for each employee.

The agent combines the results:

The training officer can approve routing reminders directly from the agent. Each action is logged with the officer's identity, the action taken, and the employees affected — creating a complete audit trail.

7. Example: New Employee Onboarding

An HR specialist asks:

"How is the new hire in the Legal division doing with onboarding?"

The agent checks three systems:

1. HRIS MCP server — get_onboarding_checklist(employee_id) returns checklist progress: background check complete, I-9 verified, direct deposit set up, emergency contacts pending.
2. LMS MCP server — get_employee_training_compliance(employee_id, "onboarding") returns mandatory training status: ethics training complete, cybersecurity awareness complete, records management not started, FOIA training not started.
3. Ticketing MCP server — get_provisioning_status(employee_id) returns IT provisioning status: PIV badge issued, laptop delivered, VPN access approved, legal case management system access pending IT security review.

The HR specialist now has a single view across three systems without logging into any of them individually. The blocked item is immediately visible, with a ticket number to follow up on.

8. Example: Citizen Services Query

A citizen-facing agent receives:

"What's the status of my application?"

The agent calls two systems:

1. Case Management MCP server — get_application_status(citizen_id, application_id) returns application status, required documents, next steps, and internal case notes.
2. Citizen Services MCP server — get_citizen_interaction_history(citizen_id) returns contact history and prior interactions.

The agent combines the results and applies PII minimization — the citizen sees only what they are authorized to see about their own case. Internal case notes, reviewer assignments, and internal scoring criteria are filtered out before the response is returned.

PII minimization is critical here. The case management system holds internal notes, reviewer identities, and scoring details that the citizen must not see. The MCP server's output filtering ensures only citizen-authorized fields are returned. The broker's policy layer verifies the citizen's identity before any data is returned.

9. Security Checklist for Government

Before deploying MCP infrastructure in a government environment, verify each of the following:

10. Next Steps

If your agency is ready to start connecting systems to AI agents with full data sovereignty:

• Contact us to schedule a technical walkthrough with your IT leadership team.
• Read the MCP Servers service page for details on how ibl.ai builds and deploys MCP infrastructure.
• Return to the Government AI Solutions overview for the full picture of ibl.ai's public sector capabilities.