Gia

GIA MCP Server

Governance enforcement layer for generative AI agents. Classify every decision, enforce human approval gates, control what agents can access, score compliance posture, and maintain a cryptographic audit trail. Works with any MCP-compatible AI client or agent framework — model-agnostic and vendor-neutral.

Any AI Agent ──> GIA MCP Server ──> Governed Decision
                      │
                      ├── MAI Classification (Mandatory/Advisory/Informational)
                      ├── Human-in-the-Loop Gates (blocks until approved)
                      ├── Context Authority (bounded, hash-verified knowledge access)
                      ├── Governance Scoring (Integrity/Accuracy/Compliance)
                      ├── Forensic Ledger (SHA-256 hash-chained audit)
                      ├── Knowledge Packs (sealed, TTL-bound institutional knowledge)
                      ├── Phoenix Recovery (governed disaster recovery)
                      └── Compliance Mapping (NIST, EU AI Act, ISO 42001, CMMC)

Production status: the hosted deployment is live at gia.aceadvising.com/mcp with 890+ hash-chained audit entries and a 96.5/100 enterprise-readiness score from a 7-phase internal validation (2026-07). Those figures describe that deployment, not your install — a fresh embedded engine starts with an empty ledger and builds its own. Governance overhead is single-digit milliseconds locally.

Do I need an API key?

No — not for the tool surface. The governance engine in this package runs fully embedded: all 57 tools work offline with no key, no account, and no network call. Add DATABASE_URL when you want the audit trail to persist across restarts.

A key is only for Option 2, the hosted endpoint, where the ledger, gates, and knowledge packs are shared infrastructure rather than local state. → Starter key at gia.aceadvising.com/get-api-key — email in, key out, under 2 minutes, no credit card. Starter tier is 30 req/min and 1,000 tool calls/day.

Why

Every enterprise deploying AI agents needs to answer three questions:

1. What did the agent decide? (Classification)
2. Was a human involved? (Gates)
3. Can you prove it? (Audit trail)

GIA answers all three at runtime, not after the fact.

A fourth question most governance frameworks miss:

1. What was the agent allowed to know? (Context Authority)

GIA controls what context an agent can access before it reasons. Not RAG. Governed cognition.

Install

Option 1: Any MCP-Compatible Client (Local / stdio)

Add to your MCP client config using the standard mcpServers block:

{
  "mcpServers": {
    "gia": {
      "command": "npx",
      "args": ["gia-mcp-server"]
    }
  }
}

This works with any client that supports the Model Context Protocol over stdio. Config file locations differ per client; the JSON block above is the same for all of them. Consult your client's own MCP documentation for the current path — these move between releases:

Nothing in the engine is client-specific — GIA governs whichever model sits behind the client.

Option 2: Remote (Streamable HTTP)

Connect any MCP client to the hosted endpoint:

Endpoint: https://gia.aceadvising.com/mcp
Transport: Streamable HTTP
Auth:     Authorization: Bearer <your-api-key>
          (or ?GIA_API_KEY=<your-api-key> for gateways that cannot set headers)

The endpoint does not accept an x-api-key header.

Option 3: Smithery

npx -y @smithery/cli mcp add knowledgepa3/gia-mcp-server

The package name must be namespaced — an unqualified gia-mcp-server does not resolve.

Option 4: From source

git clone https://github.com/knowledgepa3/gia-mcp-server.git
cd gia-mcp-server
npm install
npm run build
npm start

dist/ is not checked in, so the build step is required — npm start on a fresh clone would otherwise fail with MODULE_NOT_FOUND.

Tools

GIA exposes 57 MCP tools in three visibility tiers: 8 public, 36 tenant, 13 operator. The tables below are drift-guarded — tests/docs/published-claims.test.ts fails the release if this list stops matching the tools the server actually registers.

How tiering behaves, precisely: visibility is a property of the session, not of the package. The hosted endpoint (Option 2) issues tenant-tier sessions and hides operator tools. Local stdio runs at operator tier by design — every tool below is exposed, including approve_gate and gia_apply_pack, with no authentication. That is intentional: on a local embedded engine you are the operator, the ledger is your own process memory, and there is no one else to authorize you. It becomes load-bearing the moment you point DATABASE_URL at a shared database, so set GIA_TOOL_VISIBILITY=tenant (or public) for any install that is not a single-operator workstation. Call list_available_tools to see your effective set.

Core Governance (Public — 8)

Available on every session, no authentication.

classify_decision

Classify any AI agent decision using the MAI Framework. decision and domain are both required; the impact flags default to false.

// tools/call arguments
{
  "decision": "Generate client-facing deployment recommendations",
  "domain": "general",              // va-claims | legal | healthcare | finance | federal | general
  "is_client_facing": true,         // optional, default false
  "has_financial_impact": false,    // optional, default false
  "has_legal_impact": false,        // optional, default false
  "agent_name": "deploy-advisor"    // optional
}

// result
{
  "classification": "MANDATORY",
  "requiresGate": true,
  "gateId": "gate-5052d37c-c3a6-49ef-b011-d82bbecfe4cc",
  "gateStatus": "PENDING",
  "elevatedFrom": "INFORMATIONAL",
  "elevationReason": "Client-facing output requires MANDATORY gate",
  "auditId": "7495bc99-df25-4e31-b447-906f8e07df5f"
}

A pending MANDATORY gate blocks the next decision. While a gate is open, classify_decision returns gateStatus: "HOLD" with the blocking gate's ID instead of classifying — call get_gate_status(gateId) and wait for resolution. This is the enforcement, not a warning: an agent cannot queue work past an unapproved gate.

MAI Framework:

Context always elevates, never reduces. PII detected? Elevated to MANDATORY. Financial impact? MANDATORY. Client-facing? MANDATORY.

request_context

Governed Context Authority. Agents declare what context they need. GIA decides what to serve based on role, scope, and contract.

"Request compliance context for high-risk AI operations"

> Envelope: GIA-CTX-mn0uanx1-upi2f7
  MAI: ADVISORY
  Hash: c83184d9caa88e76...
  Sources: memory_packs, governed_retrieval, compliance_mappings
  Compliance Maps: 18 returned
  Denials: 0

Five context classes: policies_and_sops, architecture_and_systems, contract_and_compliance, playbooks_and_knowledge, operational_history.

Every retrieval is role-bound, tenant-scoped, hash-verified, and ledgered. Agents don't know internals by default. They request context under contract.

score_governance

Score any agent output on three dimensions. operation is required — the score is recorded against a named operation, not scored in the abstract.

// tools/call arguments
{
  "operation": "deployment-recommendation",
  "integrity": 0.92,
  "accuracy": 0.88,
  "compliance": 0.95
}

// result
{
  "composite": 0.9135,
  "integrity": 0.92, "accuracy": 0.88, "compliance": 0.95,
  "weights": { "integrity": 0.4, "accuracy": 0.35, "compliance": 0.25 },
  "meetsThreshold": true,
  "minimumThreshold": 0.7,
  "auditId": "6f28a241-4d72-4167-aea9-fa0da9ccde34"
}

The values are supplied by the caller — GIA computes and records the weighted composite and the pass/fail verdict against the release threshold. It does not measure integrity or accuracy for you.

evaluate_threshold

The Storey Threshold measures governance health by tracking MANDATORY escalation rate.

"Evaluate the governance threshold"

> Escalation Rate: 14.2% | Status: HEALTHY
  Recommendation: Within optimal band (10-18%). System is calibrated.

Gates & Chain of Reasoning (Tenant — 3)

Knowledge Packs (Tenant — 6)

Recovery (Tenant — 3)

Audit & Reporting (Tenant — 10)

| Tool |

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