{"slug":"monte-carlo-monitoring-advisor","title":"monte-carlo-monitoring-advisor","summary":"Analyze data coverage, create monitors for warehouse tables and AI agents. Covers coverage gaps, use-case analysis, data monitor creation, and agent observability.","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-09-20T07:56:52.647259Z","repo":{"url":"https://github.com/monte-carlo-data/mc-agent-toolkit","stars":92,"forks":5,"license":"Apache-2.0","updatedAt":"2026-09-27T17:08:21Z"},"bodyHtml":"

Monte Carlo Monitoring Advisor Skill

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Analyze data coverage, create monitors for warehouse tables and AI agents. Walks users through warehouse discovery, use-case exploration, coverage gap analysis, data monitor creation, and agent observability — all through natural conversation. This single skill handles all monitoring needs: coverage analysis, data quality monitors (metric, validation, custom SQL, comparison, table), and AI agent monitors (metric, evaluation, trajectory, validation).

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Editor & Stack Compatibility

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The skill works with any AI editor that supports MCP and the Agent Skills format — including Claude Code, Cursor, and VS Code.

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All warehouses supported by Monte Carlo work with the monitoring advisor. The skill validates table and column references against your actual warehouse schema via the Monte Carlo API.

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Prerequisites

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Setup

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Via the mc-agent-toolkit plugin (recommended)

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Install the plugin for your editor — it bundles the skill, hooks, MCP server, and permissions automatically. See the main README for editor-specific instructions.

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Standalone

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  1. Configure the Monte Carlo MCP server:

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    claude mcp add --transport http monte-carlo-mcp https://mcp.getmontecarlo.com/mcp\n
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    2. \n

  2. Install the skill:

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    npx skills add monte-carlo-data/mc-agent-toolkit --skill monitoring-advisor\n
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    3. \n

  3. Authenticate: run /mcp in your editor, select monte-carlo-mcp, and complete the OAuth flow.

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    4. \n

  4. Verify: ask your editor \"Test my Monte Carlo connection\" — it should call test_connection and confirm.

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    5. \n
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\nLegacy: header-based auth (for MCP clients without HTTP transport)\n

If your MCP client doesn't support HTTP transport, use .mcp.json.example with npx mcp-remote and header-based authentication. See the MCP server docs for details.

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How to use it

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Ask your AI editor about your monitoring coverage — describe what you want to understand or protect. The skill guides the agent through warehouse discovery, use-case analysis, coverage gap identification, and monitor creation. No special commands needed.

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Example prompts

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What it does

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  1. Discovers your warehouses, use cases, and AI agents
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  2. Analyzes coverage — which tables are monitored, which aren't, and which have active anomalies
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  3. Prioritizes gaps by criticality, importance score, and anomaly activity
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  4. Creates data quality monitors (metric, validation, custom SQL, comparison, table) with full parameter validation
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  5. Creates AI agent monitors (metric, evaluation, trajectory, validation) for agent observability
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  6. Generates monitors-as-code YAML ready for deployment
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Deploying generated monitors

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When the advisor generates a monitor, it returns MaC YAML. Deploy with:

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montecarlo monitors apply --dry-run    # preview\nmontecarlo monitors apply --auto-yes   # apply\n
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Your project needs a montecarlo.yml config in the working directory:

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version: 1\nnamespace: <your-namespace>\ndefault_resource: <your-warehouse-name>\n
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