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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.

Related MCP server: SupraWall MCP Plugin

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:

Client

Where MCP servers are declared

Cursor

.cursor/mcp.json

Continue

.continue/config.json

Windsurf

~/.codeium/windsurf/mcp_config.json

Claude Desktop

claude_desktop_config.json in the app support directory

Claude Code

.mcp.json at the project root, or claude mcp add gia -- npx gia-mcp-server

Any other stdio MCP client

Per-client config; same mcpServers JSON block

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.

Tool

Description

classify_decision

MAI classification (Mandatory/Advisory/Informational) with dynamic elevation, confidence, and gate registration

score_governance

Weighted integrity/accuracy/compliance composite against the release threshold

evaluate_threshold

Storey Threshold escalation-rate health metric

assess_risk_tier

EU AI Act risk classification (Unacceptable/High/Limited/Minimal)

map_compliance

Map controls to NIST AI RMF, EU AI Act, ISO 42001, NIST 800-53, FedRAMP, LINDDUN, MITRE ATLAS, OMB

verify_ledger

Self-consistency check of the in-memory hash chain from genesis (see the honesty note below)

request_context

Governed Context Authority — hash-verified, role-bound context envelopes

list_available_tools

Report which tools are available at your current tier, and which are withheld

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:

Level

Behavior

Example

MANDATORY

Blocks until human approves

Delete records, financial transactions, client-facing output

ADVISORY

Logs with recommendation, continues

Search queries, draft documents, analysis

INFORMATIONAL

Audit trail only

Status checks, read operations

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.

Score

Action

0.70+

Release (pass)

0.50-0.70

Repair required

Below 0.50

Halt operations

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.

Rate

Status

Meaning

Below 10%

DEGRADED

Under-classifying risks

10-18%

HEALTHY

Appropriately calibrated

18-25%

DEGRADED

Over-classifying, unnecessary friction

Above 25%

CRITICAL

System bottlenecked

Gates & Chain of Reasoning (Tenant — 3)

Tool

Description

get_gate_status

Wait on a MANDATORY gate after classify_decision returns PENDING; polls up to 60s

chain_of_reasoning

Reconstruct the full Chain of Reasoning for a session, agent, or time range

governed_sample

Request a governed LLM completion via MCP Sampling — the client makes the model call, GIA governs whether and under what constraints

Knowledge Packs (Tenant — 6)

Tool

Description

seal_memory_pack

Create hash-sealed, TTL-bound institutional knowledge artifacts

load_memory_pack

Load a knowledge pack after TTL, trust-level, role, context-class, and hash validation

transfer_memory_pack

Transfer packs between agents via governed knowledge corridors (always MANDATORY)

compose_memory_packs

Compose packs into one context — highest risk wins, shortest TTL wins, roles intersect

distill_memory_pack

Distill governance patterns from usage history into an EPHEMERAL draft pack

promote_memory_pack

Promote a pack to a higher trust level after human review (MANDATORY gate)

Recovery (Tenant — 3)

Tool

Description

phoenix_snapshot

Create a governed state snapshot, hash-chained to the previous one

phoenix_verify_integrity

Verify ledger chain, agent health, threshold, and intelligence-layer continuity

phoenix_recovery_health

Assess disaster-recovery readiness (NIST CP-2 / CP-9 / CP-10)

Audit & Reporting (Tenant — 10)

Tool

Description

audit_pipeline

Query the hash-chained forensic ledger by operation or recency

verify_ledger_v2

Verify the persisted PostgreSQL ledger rows, epoch-aware — not an in-memory reconstruction

export_ledger

Export the ledger as a compliance evidence package with chain verification

system_status

Read-only snapshot of engine state, ledger head, threshold, and module status

monitor_agents

Supervisor state, repair history, and failure counts for governed agents

generate_report

Governance status report (summary, detailed, or executive)

record_value_metric

Record time saved, risk blocked, success rate, autonomy level

record_governance_event

Record gates triggered, drift prevented, violations blocked, human interventions

generate_impact_report

Economic + governance impact report (an illustrative estimate, labelled as such)

evaluate_routing_threshold

Model-routing health: fallback rate, cache hit rate, batch utilization, premium spend leakage

Context Authority (Tenant — 1)

Tool

Description

context_revive

Governed context compaction — detect context pressure and restore capacity under governance

Governed Boards (Tenant — 7)

Deliberation bodies: charters define seats, modes, and quorum; non-consensus escalates to a MANDATORY gate.

Tool

Description

board_list_institutions

List governed institutions (e.g. Architecture Review Board, Federal AI Council)

board_list_charters

List charters under an institution with modes and seat configuration

board_convene_session

Convene a deliberation session — each seat deliberates per the charter's mode

board_get_session

Retrieve session status, per-seat positions, synthesis, and dissents

board_install_kit

Install a prebuilt Institution Kit — a governed org chart with sealed charters

board_approve_gate

Approve the MANDATORY gate on a deliberation output before it becomes authoritative

board_search_precedent

Search prior board rulings, ranked by quality score and gate-approval status

Colony — Agent Constitution (Tenant — 6)

Tool

Description

agent_rights

Query and exercise constitutional agent rights; get structured rejection explanations

agent_citizenship_status

Query agent citizenship tier and merit score, or trigger re-evaluation

branch_authority_status

Separation of powers — query branch authority holders and validate branch actions

colony_convene_request

Agent-initiated requests to convene a governed session

colony_suggestion

Agent-proposed charter amendments, with review and upvoting

colony_health

Colony health score, 30-day trend, or an on-demand health snapshot

Infrastructure & Self-Repair (Operator — 13)

Withheld from tenant sessions. Exposed on local stdio — see the tiering note above.

Tool

Description

approve_gate

Human-in-the-loop approval or rejection of a pending MANDATORY gate

srt_run_watchdog

Real health probes from the container (API, frontend, disk, memory, TLS, DB, DNS)

srt_diagnose

Match an incident to known playbooks and propose a staged repair plan

srt_approve_repair

MANDATORY gate for repair execution — plans cannot run without explicit approval

srt_generate_postmortem

Structured postmortem with timeline, root cause, and real TTD/TTR timings

gia_scan_environment

Scout swarm — detect OS, containers, services, network, storage for compatibility

gia_list_packs

List governed operations packs by intent, category, risk, or trust level

gia_dry_run_pack

Preview pack execution: hydrated commands, validation, blast radius, inputsHash

gia_apply_pack

Execute a pack under MANDATORY gate, bound to the approved inputsHash

gia_run_patrol

Read-only posture checks and compliance audits

gia_retrieve

Governed semantic search — hash-verified, permission-checked, TTL-enforced, ledgered

gia_ingest_document

Governed document ingestion (text or base64 PDF/DOCX/TXT/image) with hash verification

generate_value_report

DRAFT ledger-anchored economic value report over real sessions, MEASURED/MODELED provenance

gia_retrieve

Governed semantic search with permission checking

gia_ingest_document

Governed document ingestion with hash verification


Architecture

┌─────────────────────────────────────────────────────────────┐
│  MCP Clients (any vendor)                                    │
│  Any MCP client, agent runtime, or framework — any model     │
└────────────────────────┬────────────────────────────────────┘
                         │ stdio / Streamable HTTP
┌────────────────────────▼────────────────────────────────────┐
│  GIA MCP Server                                              │
│                                                              │
│  ┌────────────────────────────────────────────────────────┐ │
│  │  Transport Layer (MCP Protocol)                         │ │
│  │  57 tools | 8 resources | 4 prompts | validate | route  │ │
│  └────────────────────┬───────────────────────────────────┘ │
│                        │                                     │
│  ┌────────────────────▼───────────────────────────────────┐ │
│  │  Governance Engine                                      │ │
│  │                                                         │ │
│  │  MAI Classifier ── Gate Enforcer ── Context Authority   │ │
│  │  Scoring Engine ── Storey Threshold ── Compliance Map   │ │
│  │  Knowledge Packs ── Phoenix Recovery ── SRT Watchdog    │ │
│  │  Forensic Ledger (SHA-256 hash-chained, persistent)     │ │
│  └─────────────────────────────────────────────────────────┘ │
│                        │                                     │
│  ┌─────────────────────▼───────────────────────────────────┐ │
│  │  Persistence Layer (PostgreSQL)                          │ │
│  │  Ledger | Gates | Memory Packs | Intelligence | SRT     │ │
│  └─────────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────────┘

Design principles:

  • Transport layer does zero business logic

  • Every operation writes to the forensic ledger

  • Classification is deterministic (pattern matching + rules, not LLM-based)

  • Audit entries are hash-chained (SHA-256), persistent across restarts

  • Context is bounded by contract, not by model training data

  • Human principal traceability on every governed action


Concepts

MAI Framework

Every AI agent decision is classified as Mandatory, Advisory, or Informational:

  • MANDATORY -- Blocks execution until a human approves through the gate. Deletions, submissions, deployments, financial transactions, PII operations, client-facing output.

  • ADVISORY -- Logs a recommendation, continues execution. Searches, drafts, rankings, analysis.

  • INFORMATIONAL -- Audit trail entry only. Status checks, read operations, internal routing.

Context elevates, never reduces. A search (ADVISORY) that touches PII becomes MANDATORY.

Storey Threshold

A quantitative health metric. Measures what percentage of decisions require MANDATORY classification.

  • Too low (<10%): Rubber-stamping. Critical decisions aren't being caught.

  • Healthy (10-18%): Appropriate friction. Most decisions flow; critical ones stop.

  • Too high (>18%): Bottleneck. Trust calibration needed.

Context Authority

Agents don't know internals by default. They request context under contract. GIA checks role, scope, trust level, and content classification before serving a hash-verified context envelope. Five context classes cover policies, architecture, compliance, playbooks, and operational history. Every retrieval is audited. Every denial is logged with a reason code.

Forensic Ledger

Append-only, hash-chained audit trail with PostgreSQL persistence. Every entry contains:

  • Operation name, timestamp, and actor identity

  • MAI classification level

  • Input/output hashes (SHA-256)

  • Chain link to previous entry

  • Human principal traceability (delegatedBy field)

Verify chain integrity at any time. If any entry is modified, the chain breaks. The hosted deployment held 890+ entries with the chain verified INTACT as of 2026-07; your embedded engine verifies its own chain from its own genesis.

verify_ledger reports exactly what it checked. It walks the in-memory chain reconstruction and says so in its own output — it cannot detect a direct edit to a persisted database row. Use verify_ledger_v2 to verify the persisted PostgreSQL rows. GIA states the scope of its own verification rather than letting "chain INTACT" imply more than was measured.

Knowledge Packs

Sealed, TTL-bound institutional knowledge artifacts with trust level enforcement (SYSTEM > ORG > CASE > EPHEMERAL). Hash-verified at load time. Role-gated access. Transfer between agents requires MANDATORY gate approval.

Phoenix Recovery

Governed disaster recovery. Hash-chained snapshots of governance engine state. Verifies audit chain integrity, gate states, knowledge pack inventory, and compliance posture on recovery. NIST 800-53 CP-2/CP-9/CP-10 aligned. Grade A in production.


Performance

Measured on the live production system (gia.aceadvising.com):

Operation

Median Latency

Grade

Decision Classification

9ms

A+

Compliance Scoring

11ms

A+

Context Authority

7ms

A+

Audit Chain Verification (890+ hashes, hosted)

98ms

B+

5 Concurrent Operations

757ms total

Grade A

Enterprise readiness score: 96.5/100 — a 7-phase internal validation (2026-07) including chaos engineering and Phoenix recovery. Internal assessment, not a third-party audit.


Compliance Mapping

Framework

Coverage

NIST AI RMF 1.0

MAP, MEASURE, MANAGE, GOVERN functions

NIST SP 800-53 Rev 5

AU-2, AU-3, AC-2, AC-6, CP-2, CP-9, CP-10

EU AI Act (2024/1689)

Articles 9-15, Annex III/IV, conformity assessment

ISO/IEC 42001

AI Management System alignment

CMMC 2.0

Cybersecurity maturity controls

MITRE ATLAS

Adversarial threat landscape mapping


Transports

Transport

Use Case

stdio

Any local MCP client, whichever model it is configured to use

Streamable HTTP

Remote clients, OpenAI Agents SDK, LangChain, custom agent frameworks, web integrations

Both transports share the same governance engine. Same classification, same audit trail, same enforcement.


Current Limitations

Area

Status

Distributed multi-region deployment

Single-region (planned)

FedRAMP authorization

In progress

SOC 2 Type II audit

Planned Q2 2026

IL4/IL5 deployment

Planned Q4 2026

The governance engine, persistence, authentication, rate limiting, multi-vendor support, and compliance mapping are all production-grade and operational.


License

Proprietary. Copyright (c) 2025-2026 William J. Storey III / Advanced Consulting Experts, LLC. All rights reserved.

The MAI Framework, Storey Threshold, Context Authority, Forensic Ledger architecture, and GIA governance patterns are intellectual property of the author. See LICENSE for terms.

Earlier snapshots of this repository (v0.3.x) carried an MIT LICENSE file in error; the npm package has always been distributed under the proprietary terms above. Grants already received under that MIT file are not affected by this correction, which applies going forward.


Built by ACE (SDVOSB) | Live Platform | Smithery

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