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Bigeye MCP Server

from bigeyedata

Interact with Bigeye's data quality monitoring platform via its Datawatch API. Supports dynamic API key authentication.

🔥🔥🔥✓ VerifiedFreeAdvanced setup
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Bigeye MCP Server

An MCP (Model Context Protocol) server that provides tools for interacting with the Bigeye Data Observability platform.

Telemetry

To help us improve the server, anonymous usage analytics (tool name, duration, success/error) are sent to Bigeye. No arguments, results, or data are ever collected. Set BIGEYE_TELEMETRY=false to turn it off.

Container Modes

Long-Lived Container (Recommended)

Uses mcp-wrapper.sh + bigeye-mcp.sh with docker compose. The container stays running and Claude Desktop connects via docker exec.

Note: mcp-wrapper.sh relies on Docker Compose automatically reading the .env file from the project directory. Make sure your .env file is in the same directory as docker-compose.yml.

{
  "mcpServers": {
    "bigeye": {
      "command": "/absolute/path/to/mcp-wrapper.sh"
    }
  }
}

Ephemeral Container

A fresh container spins up for each Claude Desktop session and is removed when done.

{
  "mcpServers": {
    "bigeye": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-e", "BIGEYE_API_KEY=your_api_key_here",
        "-e", "BIGEYE_BASE_URL=https://app.bigeye.com",
        "-e", "BIGEYE_WORKSPACE_ID=your_workspace_id_here",
        "-e", "BIGEYE_DEBUG=false",
        "bigeye-mcp-server:latest"
      ]
    }
  }
}

Container Management

The bigeye-mcp.sh script manages the long-lived container:

./bigeye-mcp.sh start     # Start the container
./bigeye-mcp.sh stop      # Stop the container
./bigeye-mcp.sh restart   # Restart the container
./bigeye-mcp.sh status    # Show container status
./bigeye-mcp.sh logs      # Follow container logs
./bigeye-mcp.sh rebuild   # Rebuild image and recreate container
./bigeye-mcp.sh clean     # Remove container and volumes

Available Tools

Issue Management

  • list_issues — List data quality issues across the workspace (filter by status, schema, or assignee_ids)
  • get_current_user — Get the authenticated user (id, email, name, workspaces); pair with list_issues(assignee_ids=[id]) to find issues assigned to you
  • get_issue — Get full details for a single issue by its internal ID
  • search_issues — Find issues by their display name/number (e.g. "10921")
  • list_related_issues — List issues related to a given issue via lineage
  • list_table_issues — List data quality issues for a specific table by name
  • update_issue — Update an issue's status, priority, or add a timeline message
  • create_incident — Create an incident by merging related issues
  • delete_incident_members — Remove issues from an incident
  • get_resolution_steps — Get recommended resolution steps for an issue

Metrics & Quality

  • list_table_metrics — List all metrics (monitors) configured on a table
  • list_table_level_metrics — List metric types that are table-level (vs column-level)
  • create_metric — Create a new metric (monitor) on a table with validation, enum mapping, and column-type compatibility checks
  • get_table_profile — Get data profile report including column statistics and distribution
  • create_profile_job — Queue a new data profiling job for a table
  • get_profile_job_status — Check the status of a profiling job

Sensitive Data Scanning

  • list_data_classes — List data classification categories (e.g., "Email Address", "US SSN") with sensitivity levels
  • get_scan_findings — Get column-level classification scan results showing where sensitive data was detected

Data Catalog

  • search_schemas — Search the data catalog for schemas by name (returns ids + warehouse)
  • search_tables — Search the data catalog for tables by name (returns ids, schema + warehouse)
  • search_columns — Search the data catalog for columns by name (returns ids, type + parent table)

Data Lineage

  • get_lineage_graph — Get the full lineage graph (upstream/downstream/both) from a starting node
  • get_lineage_node — Get details for a specific lineage node
  • list_lineage_node_issues — List issues for a lineage node by its node ID
  • search_lineage_nodes — Find lineage node IDs by path pattern (e.g. "WAREHOUSE/SCHEMA/TABLE")
  • lineage_explore_catalog — Explore tables in Bigeye's catalog
  • lineage_delete_node — Delete a custom lineage node

Root Cause & Impact Analysis

  • get_upstream_root_causes — Analyze upstream lineage to identify root causes of issues
  • get_downstream_impact — Analyze downstream impact of issues at a lineage node
  • get_issue_lineage_trace — Trace a data quality issue end-to-end through lineage
  • list_report_upstream_issues — List upstream issues affecting a BI report or dashboard

Agent Lineage Tracking

  • lineage_track_data_access — Track data assets accessed by an AI agent
  • lineage_commit_agent — Commit tracked data access to Bigeye's lineage graph
  • lineage_get_tracking_status — Get the current status of lineage tracking
  • lineage_clear_tracked_assets — Clear all tracked data assets without committing
  • lineage_cleanup_agent_edges — Clean up old lineage edges for the AI agent

Data Dimensions

  • list_dimensions — List all data-quality dimensions with their metric type mappings
  • get_dimension — Get full details for a single dimension by ID
  • create_dimension — Create a new Data Dimension
  • update_dimension — Update a dimension's name or description
  • delete_dimension — Delete a Data Dimension
  • get_table_dimension_coverage — Analyze dimension coverage gaps for a table (recommended for "what monitoring is missing?")
  • get_column_dimension_coverage — Analyze dimension coverage for specific columns in a table

Tags

  • list_tags — List or search workspace tags
  • create_tag — Create a new tag with optional color
  • update_tag — Update a tag's name or color
  • delete_tag — Delete a tag
  • tag_entity — Apply a tag to any entity (metric, table, column, etc.)
  • untag_entity — Remove a tag from an entity
  • list_entity_tags — List all tags on a specific entity

System

  • get_health_status — Check the health and connectivity of the Bigeye API
  • list_resources — List all available MCP resources
  • list_data_sources — List all data sources/warehouses connected to Bigeye

Resources & Prompts

Resources:

  • bigeye://auth/status — Current authentication status
  • bigeye://health — API health status
  • bigeye://config — Current server configuration
  • bigeye://issues — All issues from the configured workspace
  • bigeye://issues/active — Active issues with filtering
  • bigeye://issues/recent — Recently resolved or updated issues

Prompts:

  • authentication_flow — Guide for setting up authentication
  • check_connection_info — Guide for verifying API connection
  • merge_issues_example — Examples for merging issues
  • lineage_analysis_examples — Examples for lineage analysis

Agent Lineage Tracking

The server includes comprehensive lineage tracking for AI agents. Use the lineage_track_data_access tool to record data assets accessed during an agent session, then lineage_commit_agent to persist them to Bigeye's lineage graph. See the tool descriptions above for full details.

Testing

# Run basic container tests
./scripts/test.sh

# Run basic tests + MCP protocol tests
./scripts/test.sh --mcp

# Run MCP protocol tests standalone (with optional --debug)
./scripts/test-mcp-protocol.sh

See tests/README.md for details on the test suite.