Bigeye MCP Server

What it is

An MCP server for the Bigeye data observability platform, shipped as a Docker image. It reaches most of the platform: issues and incidents, metrics and profiling, the data catalog, lineage in both directions, dimension coverage, tags and the business glossary.

What you get

• Issues — list_issues, get_issue, search_issues by display number, list_table_issues, list_related_issues via lineage, update_issue, create_incident by merging related issues, get_resolution_steps
• get_current_user returns your id, which pairs with list_issues(assignee_ids=[id]) to find what is assigned to you
• Metrics and profiling — list_table_metrics, create_metric with validation and column-type compatibility checks, get_table_profile, create_profile_job, get_profile_job_status
• Lineage — get_lineage_graph upstream, downstream or both, search_lineage_nodes by path pattern, get_lineage_node, list_lineage_node_issues
• Root cause and impact — get_upstream_root_causes, get_downstream_impact, get_issue_lineage_trace, list_report_upstream_issues for a BI report or dashboard
• Catalog — search_schemas, search_tables, search_columns, list_data_sources
• Sensitive data — list_data_classes, get_scan_findings showing where classified data was detected
• Dimensions, tags and glossary — including get_table_dimension_coverage for answering "what monitoring is missing?"
• Agent lineage tracking — record the data an agent touched with lineage_track_data_access, then lineage_commit_agent to persist it into the lineage graph

Requirements

Docker, plus BIGEYE_API_KEY generated under Settings > API Keys, BIGEYE_BASE_URL for your instance, and BIGEYE_WORKSPACE_ID, which is the number after /w/ in your Bigeye URL. BIGEYE_DEBUG turns on verbose logging and BIGEYE_TELEMETRY=false disables anonymous usage analytics — which record tool name, duration and success only, never arguments or results. The long-lived container path uses mcp-wrapper.sh with docker compose and expects the .env file beside docker-compose.yml; an ephemeral container per session is the alternative. Local development without Docker needs Python 3.12+.

Setup effort

Run a container — build the image from the repository, then point your client at it

Tools you get

61 tools named in the project's documentation.

Requires

• BIGEYE_API_KEY in your environment
• BIGEYE_BASE_URL in your environment
• BIGEYE_WORKSPACE_ID in your environment

Where it runs

The documented client runs the server on your machine.