MCP Server

The AnomalyArmor MCP server lets AI assistants like Claude Code and Cursor interact with your data observability platform through natural language.

Connection Methods

Method	Auth	Best For
Remote (Recommended)	Sign in with your AnomalyArmor account	Zero setup, always up to date
Local	API key	Air-gapped environments, custom setups

Remote Server (Recommended)

Connect directly to AnomalyArmor’s hosted MCP server. No installation required. You sign in with your existing AnomalyArmor account through OAuth.

Claude Code

claude mcp add anomalyarmor --transport http https://mcp.anomalyarmor.ai/mcp

Claude Code will open a browser window to sign in the first time you use a tool.

Cursor

Add to your Cursor MCP configuration: macOS: ~/Library/Application Support/Cursor/mcp.json Linux: ~/.config/Cursor/mcp.json Windows: %APPDATA%\Cursor\mcp.json

{
  "mcpServers": {
    "anomalyarmor": {
      "url": "https://mcp.anomalyarmor.ai/mcp"
    }
  }
}

Restart Cursor after saving. You will be prompted to sign in when you first use a tool.

Other MCP Clients

Any MCP client that supports the streamable-http transport can connect:

URL: https://mcp.anomalyarmor.ai/mcp
Transport: streamable-http
Auth: OAuth 2.1 (automatic)

The server publishes standard OAuth 2.1 discovery metadata at https://mcp.anomalyarmor.ai/.well-known/oauth-protected-resource.

Local Server (API Key)

For environments where you need a local MCP server (air-gapped networks, custom configurations), install and run the server locally with an API key.

Install

pip install armor-mcp

If the package is not yet on PyPI, install from source:

pip install git+https://github.com/anomalyarmor/agents.git#subdirectory=armor-mcp

Configure

Add to your AI tool’s MCP settings:

{
  "mcpServers": {
    "anomalyarmor": {
      "command": "armor-mcp",
      "env": {
        "ANOMALYARMOR_API_KEY": "your-api-key"
      }
    }
  }
}

Getting Your API Key

Go to Settings > API Keys in the AnomalyArmor dashboard
Click Create API Key
Name it (e.g., “Claude Code MCP”)
Copy the key (it won’t be shown again)

Available Tools

Once connected, your AI assistant has access to 40+ tools across these categories:

Health and Briefings

Tool	Description
`health_summary`	Overall health status across all assets
`get_todays_briefing`	Daily digest with key insights and alerts
`get_coverage_summary`	Monitoring coverage analysis

Alerts

Tool	Description
`list_alerts`	Query alerts with date, severity, and status filters
`get_alert_summary`	Aggregate alert counts and trends
`list_inbox_alerts`	Actionable alerts (acknowledge, resolve, dismiss)
`acknowledge_alert`	Mark an alert as seen
`resolve_alert`	Close an alert with resolution metadata
`dismiss_alert`	Dismiss a non-actionable alert
`snooze_alert`	Snooze an alert for a set duration
`preview_alerts`	Dry-run alert rules against historical data

Assets

Tool	Description
`list_assets`	List data sources with filters
`get_asset`	Get details for a specific asset
`explore`	Navigate schemas, tables, and columns within an asset. Supports hierarchical browsing (`explore(asset_id)` for schemas, `explore(asset_id, parent_path="schema")` for tables) and full-text search (`explore(asset_id, search="keyword")`)
`create_asset`	Connect a new data source
`test_asset_connection`	Test connectivity to a source
`trigger_asset_discovery`	Start schema discovery (runs as background job)

Freshness

Tool	Description
`get_freshness_summary`	Freshness overview. Supports optional `asset_id` to filter to a single source
`check_freshness`	Check a specific table’s freshness
`list_stale_assets`	Find currently stale tables
`list_freshness_schedules`	View monitoring schedules
`create_freshness_schedule`	Set up freshness monitoring. Use `setup_freshness` for bulk creation with `table_paths` and `schema_name` params
`delete_freshness_schedule`	Remove a schedule
`dry_run_freshness`	Preview what alerts a threshold would produce

Schema Monitoring

Tool	Description
`get_schema_summary`	Schema drift overview. Supports optional `asset_id` to filter to a single source
`list_schema_changes`	Recent schema changes with filters
`create_schema_baseline`	Capture current schema as baseline
`enable_schema_monitoring`	Start drift detection for one or more assets. Accepts `asset_id` (single) or `asset_ids` (bulk), interval strings (`'5m'`, `'1h'`, `'6h'`, `'1d'`) or `cron_expression` for custom schedules
`disable_schema_monitoring`	Stop drift detection
`dry_run_schema`	Preview drift detection results

Data Quality

Tool	Description
`list_metrics`	Quality metrics for an asset
`get_metrics_summary`	Metrics health overview
`create_metric`	Add a quality metric (row count, null rate, etc.)
`delete_metric`	Remove a metric
`capture_metric`	Trigger immediate metric capture
`dry_run_metric`	Preview metric alerting behavior

Validity Rules

Tool	Description
`list_validity_rules`	Validity rules for an asset
`get_validity_summary`	Passing/failing rule overview
`create_validity_rule`	Add a rule (NOT_NULL, UNIQUE, etc.)
`delete_validity_rule`	Remove a rule
`check_validity_rule`	Run a check immediately

Intelligence, Lineage, and Jobs

Tool	Description
`ask_question`	Natural language Q&A about your data
`generate_intelligence`	Trigger AI analysis for an asset
`get_lineage`	Explore upstream/downstream dependencies
`job_status`	Track progress of async operations

Tags and Classification

Tool	Description
`list_tags`	Tags for an asset
`create_tag`	Tag a table or column
`apply_tags`	Apply multiple tags to multiple objects
`bulk_apply_tag`	Apply a tag across multiple assets

Usage Examples

These examples show full terminal sessions using Claude Code with the AnomalyArmor MCP server. Each one demonstrates a real workflow from start to finish.

Morning Triage: Check Health and Act on Issues

~/warehouse $ claude

╭────────────────────────────────────────────────────────────────────╮
│ Claude Code                                                        │
╰────────────────────────────────────────────────────────────────────╯

You: What's the health of my data warehouse? Show me anything that
     needs attention.

Claude: I'll check your overall health and look for issues.

  ┌ health_summary
  │ Querying AnomalyArmor health status...
  └ Result: 47 assets monitored

  ┌ list_stale_assets
  │ Checking for freshness violations...
  └ Result: 2 stale tables found

  ┌ list_alerts
  │ Fetching unresolved alerts (last 24h)...
  └ Result: 5 alerts

Here's your morning summary:

OVERALL: NEEDS ATTENTION (2 issues)

Freshness:
  ✅ 45 tables fresh
  ❌ gold.fact_orders - 6h overdue (SLA: 2h)
  ❌ staging.orders_raw - 8h overdue (SLA: 1h)

Alerts (last 24h):
  🔴 1 critical - freshness SLA breach on gold.fact_orders
  🟡 2 warnings - schema drift on gold.fact_orders,
                    null rate spike on gold.dim_customer.email
  🔵 2 info - new schema baselines captured

Schema drift: 3 changes detected this week
Data quality: 1 anomaly (null rate)

The critical issue is the orders pipeline. staging.orders_raw
stopped updating 8 hours ago and gold.fact_orders is stale as a
result. Want me to dig into the root cause?

You: Yes, investigate the orders staleness and check if anything
     downstream is affected.

Claude: Let me trace the lineage and check downstream impact.

  ┌ check_freshness
  │ gold.fact_orders → STALE (6h overdue)
  └ Last update: yesterday 10:12 PM

  ┌ get_lineage
  │ Fetching upstream/downstream for gold.fact_orders...
  └ 1 upstream, 7 downstream

  ┌ check_freshness
  │ staging.orders_raw → STALE (8h overdue)
  └ Last update: yesterday 8:30 PM

  ┌ list_schema_changes
  │ staging.orders_raw - changes in last 7 days
  └ 1 change: discount_pct removed Tuesday 3:47 PM

Here's the full picture:

Pipeline trace:
  raw.events (✅ FRESH, updated 5m ago)
    → staging.orders_raw (❌ STALE, 8h overdue)
      → gold.fact_orders (❌ STALE, 6h overdue)

The pipeline breaks at staging.orders_raw. The upstream raw.events
table is fresh, so the issue is in the ETL between raw → staging.

Downstream impact (7 tables):
  ❌ gold.fact_payments        - depends on fact_orders
  ❌ gold.fact_refunds          - depends on fact_orders
  ❌ analytics.order_summary    - derived view
  ❌ analytics.order_funnel     - derived view
  ❌ reporting.daily_revenue    - aggregated report
  ❌ reporting.weekly_kpis      - aggregated report
  ❌ reporting.customer_ltv     - joins with dim_customer

Additional context: staging.orders_raw also had a schema change
this week (discount_pct column removed Tuesday). This may be
related to the staleness if the ETL is failing on the missing
column.

Recommended next steps:
1. Check the Airflow/dbt DAG for the staging.orders_raw job
2. Look for errors related to the discount_pct column removal
3. Once fixed, the 7 downstream tables should recover automatically

You: Acknowledge the freshness alert and resolve the schema drift
     alert with a note that the column removal was intentional.

Claude: I'll handle both alerts.

  ┌ acknowledge_alert
  │ Alert #4521 (freshness SLA breach) → acknowledged
  └ Done

  ┌ resolve_alert
  │ Alert #4518 (schema drift) → resolved
  │ Resolution: "Column removal was intentional per product change"
  └ Done

Done. The freshness alert is acknowledged (you'll still see it
until the pipeline recovers), and the schema drift alert is
resolved with your note.

Setting Up Monitoring for a New Schema

~/warehouse $ claude

You: I just added a new "analytics" schema with 8 tables. Set up
     full monitoring: freshness, schema drift, and null checks on
     any ID or email columns.

Claude: Let me discover what's in the analytics schema first.

  ┌ search_tables
  │ Searching for tables in analytics schema...
  └ Found 8 tables

  ┌ get_table_columns
  │ Reading columns for all 8 tables...
  └ 67 columns total

Found 8 tables in analytics with 67 columns. Here's what I'll set up:

Freshness monitoring (8 tables):
  analytics.order_summary      - hourly, 2h SLA
  analytics.order_funnel       - hourly, 2h SLA
  analytics.customer_cohorts   - daily, 26h SLA
  analytics.product_performance - daily, 26h SLA
  analytics.revenue_by_region  - daily, 26h SLA
  analytics.churn_analysis     - daily, 26h SLA
  analytics.funnel_conversion  - hourly, 2h SLA
  analytics.session_metrics    - hourly, 2h SLA

Schema drift monitoring: Enabled for all 8 tables

Null percentage metrics (12 columns with ID or email patterns):
  analytics.order_summary.order_id
  analytics.order_summary.customer_id
  analytics.order_funnel.user_id
  analytics.order_funnel.session_id
  analytics.customer_cohorts.customer_id
  analytics.customer_cohorts.email
  analytics.product_performance.product_id
  analytics.churn_analysis.customer_id
  analytics.churn_analysis.email
  analytics.funnel_conversion.user_id
  analytics.session_metrics.session_id
  analytics.session_metrics.user_id

I chose hourly checks for tables that look transactional and daily
for tables that look like aggregations. Want me to proceed?

You: Yes, do it all.

Claude: Setting everything up now.

  ┌ create_freshness_schedule (×8)
  │ Creating schedules for all analytics tables...
  └ 8 schedules created

  ┌ enable_schema_monitoring (×8)
  │ Enabling drift detection...
  └ 8 baselines captured

  ┌ create_metric (×12)
  │ Creating null_percentage metrics...
  └ 12 metrics created

All done:
  ✅ 8 freshness schedules (4 hourly, 4 daily)
  ✅ 8 schema drift monitors with baselines
  ✅ 12 null_percentage metrics on ID/email columns

You'll start receiving alerts if any table misses its SLA, any
schema changes are detected, or any null rates spike outside
normal ranges. The first data points will appear after the next
discovery run.

Investigating a Data Quality Issue

~/warehouse $ claude

You: The finance team says revenue numbers look wrong today.
     Something about missing discount amounts. Can you investigate?

Claude: Let me check the revenue-related tables for anomalies.

  ┌ search_tables
  │ Searching for tables matching "revenue", "order", "discount"...
  └ Found 5 relevant tables

  ┌ get_metrics_summary
  │ Checking data quality metrics on gold.fact_orders...
  └ 3 metrics tracked

  ┌ list_schema_changes
  │ gold.fact_orders - recent changes
  └ 1 change this week

  ┌ check_freshness
  │ gold.fact_orders → FRESH (updated 12m ago)
  └ No freshness issues

Found the issue. Here's what happened:

Root cause: The discount_pct column was removed from
gold.fact_orders on Tuesday at 4:12 PM.

Evidence:
  - Schema drift detected the removal Tuesday 4:12 PM
  - The column had a null_percentage metric that stopped reporting
    (no column to measure)
  - Freshness is fine - the table is still updating
  - The column was also removed from upstream staging.orders_raw
    at 3:47 PM

Impact on revenue:
  - analytics.order_summary uses SUM(discount_pct) to calculate
    discount totals - this query is now failing
  - reporting.daily_revenue references discount_pct in its
    net_revenue calculation

The table has data, but the discount column is gone. That's why
revenue looks wrong - discount amounts aren't being subtracted.

You: Was this intentional? Can you check if there's a related
     alert that someone already handled?

Claude: Let me check the alert history.

  ┌ list_alerts
  │ Filtering for schema drift alerts on gold.fact_orders...
  └ 1 alert found

  ┌ get_alert_details
  │ Alert #4518...
  └ Status: RESOLVED

There was a schema drift alert (#4518) that fired Tuesday at
4:12 PM for the discount_pct column removal. It was resolved
today at 9:15 AM with the note:

  "Column removal was intentional per product change"

So the column removal was intentional, but the downstream views
haven't been updated yet. The finance team's revenue calculations
are referencing a column that no longer exists.

Recommended:
1. Update analytics.order_summary to remove the discount_pct
   reference
2. Update reporting.daily_revenue net_revenue calculation
3. Consider adding a new discount column if discounts are still
   tracked differently

You: Tag the affected downstream tables so the data team knows.

Claude: I'll tag them.

  ┌ apply_tags
  │ Applying "needs-migration" tag...
  └ Applied to 2 tables

Tagged 2 tables with "needs-migration":
  - analytics.order_summary
  - reporting.daily_revenue

The data team can find these by asking "what tables are tagged
needs-migration?" in any AnomalyArmor agent.

Troubleshooting

Authentication Failed (Remote)

Authentication failed

Solution:

Clear your MCP auth cache and re-authenticate
In Claude Code: run /mcp and select the reconnect option
Verify you can sign in to app.anomalyarmor.ai

Authentication Failed (Local)

Error: Invalid API key

Solution: Verify your API key is correct in your MCP config. Generate a new key from Settings > API Keys if needed.

Server Not Found

Error: MCP server 'anomalyarmor' not found

Solution:

Restart your AI tool after config changes
For local mode, verify armor-mcp is installed: armor-mcp --version
Check MCP config syntax (valid JSON)

Empty Responses

If tools return empty results after successful authentication: Solution:

Verify you have data sources connected in AnomalyArmor
Try health_summary as a basic connectivity test
For local mode, ensure your API key has the correct scope

Rate Limiting

Error: Rate limit exceeded

Solution: Wait a few seconds and retry. The server handles rate limits automatically.

Next Steps

Claude Code Setup

Detailed Claude Code configuration

Cursor Setup

Cursor IDE integration

Web Agent

Use the same tools in your browser

AI Skills

Browse available skills

API Overview

API Reference

API Guides

Python SDK

CLI

Integrations

AI Agents

Connection Methods

Remote Server (Recommended)

Claude Code

Cursor

Other MCP Clients

Local Server (API Key)

Install

Configure

Getting Your API Key

Available Tools

Usage Examples

Morning Triage: Check Health and Act on Issues

Setting Up Monitoring for a New Schema

Investigating a Data Quality Issue

Troubleshooting

Authentication Failed (Remote)

Authentication Failed (Local)

Server Not Found

Empty Responses

Rate Limiting

Next Steps

Claude Code Setup

Cursor Setup

Web Agent

AI Skills

API Overview

API Reference

API Guides

Python SDK

CLI

Integrations

AI Agents

​Connection Methods

​Remote Server (Recommended)

​Claude Code

​Cursor

​Other MCP Clients

​Local Server (API Key)

​Install

​Configure

​Getting Your API Key

​Available Tools

​Usage Examples

​Morning Triage: Check Health and Act on Issues

​Setting Up Monitoring for a New Schema

​Investigating a Data Quality Issue

​Troubleshooting

​Authentication Failed (Remote)

​Authentication Failed (Local)

​Server Not Found

​Empty Responses

​Rate Limiting

​Next Steps

Claude Code Setup

Cursor Setup

Web Agent

AI Skills

Connection Methods

Remote Server (Recommended)

Claude Code

Cursor

Other MCP Clients

Local Server (API Key)

Install

Configure

Getting Your API Key

Available Tools

Usage Examples

Morning Triage: Check Health and Act on Issues

Setting Up Monitoring for a New Schema

Investigating a Data Quality Issue

Troubleshooting

Authentication Failed (Remote)

Authentication Failed (Local)

Server Not Found

Empty Responses

Rate Limiting

Next Steps