mcp-for-kibana
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Alternatives to mcp-for-kibana
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- FlicenseNot gradedqualityDmaintenanceEnables AI agents to create and manage Kibana dashboards, Lens visualizations, and data views via the Kibana Saved Objects API. It allows for programmatically listing existing resources and assembling new visualizations into dashboards through natural language commands.-
- AlicenseBqualityDmaintenanceEnables AI assistants to interact with Kibana dashboards, visualizations, and Elasticsearch data through read-only resources and executable tools for searching logs, exporting dashboards, and querying data.728 npm3MIT
- AlicenseAqualityDmaintenanceEnables LLM clients to interact with OpenSearch Dashboards, including reading saved objects, listing tenants and index patterns, and querying logs through the Dashboards API.5MIT
- AlicenseAqualityDmaintenanceEnables AI assistants to search Elasticsearch logs, retrieve log details, analyze service health, scan local codebases for APIs, and create Kibana dashboards.528 npmMIT
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to manage Kibana security alerts, rules, and exception lists via the Model Context Protocol.13MIT
- AlicenseBqualityDmaintenanceEnables interaction with Elasticsearch clusters for health checks, index management, document CRUD operations, and search via natural language.107 npmMIT
TDQS
Scored across 21 tools
Most tools have clearly distinct purposes, but add_panel and add_library_panel overlap (both add a visualization to a dashboard), and the three add_esql_*_panel tools could be seen as variants of one operation. Their descriptions do help clarify the intended use cases.
All tools use snake_case with a predictable verb_noun pattern (add_*, create_*, update_*, get_*, search_*, list_*, describe_*, export_*, import_*, resolve_*). There are no mixed conventions or inconsistent verb styles.
21 tools is heavy for this surface; the three ES|QL panel tools and separate export/import saved-object tools add niche operations that could be consolidated. While each tool has a purpose, the set is over-segmented relative to typical scoped MCP servers.
Core create/read/update workflows exist for dashboards, visualizations, data views, and panels, but deletion is entirely absent (no delete_dashboard, delete_visualization, delete_data_view, delete_panel, delete_saved_objects). There is also no explicit update_dashboard by id, leaving lifecycle gaps.