maps-geo MCP Server
Don't be the 40%. A production-ready blueprint for governing autonomous AI agents in IT operations — with executable policies, typed tool contracts, ISO 42001/NIST AI RMF mapping, and a 4-level maturity model. Vendor-neutral.
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RUN style CP fill:#e8f0fe,stroke:#2e75b6 style DP fill:#f5f5f5,stroke:#999 ``` Full Mermaid diagrams with complete detail are in [`diagrams/`](diagrams/). GitHub renders `.mermaid` files natively. ## Quick start ### 1. Review the workflow declaration [`examples/workflow.yaml`](examples/workflow.yaml) defines autonomy boundaries for a starter scope (certificate expiry, service restart, DNS misconfiguration). Adapt `scope.services` and `scope.allowed_categories` to your environment. ### 2. Define your tool contracts Use [`schemas/tool-restart-service.json`](schemas/tool-restart-service.json) as a template. For each tool your agents can invoke: - Define typed `input_schema` and `output_schema` - Add `safety` metadata: `idempotent`, `supports_dry_run`, `max_calls_per_incident`, `rollback_tool` - Set `dry_run: true` as the default The repo includes 8 tool contracts covering the most common ITSM operations. Use [`schemas/tool-schema-template.json`](schemas/tool-schema-template.json) to add your own. ### 3. Test policies against sample incidents ```bash # Install OPA (https://www.openpolicyagent.org/docs/latest/#running-opa) # macOS: brew install opa # Linux: curl -L -o opa https://openpolicyagent.org/downloads/v1.4.2/opa_linux_amd64_static chmod 755 opa && sudo mv opa /usr/local/bin/ # Test: low-risk incident (should auto-approve) opa eval \ --data policies/ \ --input examples/test-incidents/low-risk-cert-expiry.json \ "data.itsm.guardrails.decision" # Test: medium-risk incident (should require approval) opa eval \ --data policies/ \ --input examples/test-incidents/medium-risk-multi-service.json \ "data.itsm.guardrails.decision" # Test: high-risk incident (should require approval — contains "rollback_deployment" tool) opa eval \ --data policies/ \ --input examples/test-incidents/high-risk-deploy.json \ "data.itsm.guardrails.decision" ``` ### 4. Calibrate thresholds with offline replay Before enabling execution in production: 1. Collect 2–4 weeks of historical incidents for your target categories 2. Run each through the policy engine with the artefacts in this repo 3. Measure: approval rate, false-approval rate, missed-automation rate 4. Adjust `risk_score` thresholds and allowlists based on the data ### 5. Instrument with OpenTelemetry Propagate `traceparent` through every tool call (the tool contracts include a `traceparent` field for this). Emit stage-level metrics: `triage_duration`, `policy_decision`, `tool_execution_time`, `validation_result`, `rollback_count`. ## Policy pack The `policies/` directory contains a main guardrails policy and 7 focused modules: