@tocharianou/mcp-server-kibana
MCP ServerFreeKibana MCP Server
Capabilities10 decomposed
kibana api query execution via mcp protocol
Medium confidenceExecutes arbitrary Kibana REST API calls through the Model Context Protocol, translating MCP tool invocations into HTTP requests to a Kibana instance. Implements request marshaling, response parsing, and error handling to bridge Claude/LLM clients with Kibana's native API endpoints, supporting authentication via API keys or basic auth configured at server initialization.
Implements MCP as a standardized protocol bridge to Kibana's REST API, allowing Claude and other MCP-compatible clients to treat Kibana as a native tool without custom integrations. Uses MCP's tool schema system to expose Kibana endpoints dynamically.
Provides direct Kibana API access through MCP's standardized tool protocol, whereas custom Kibana integrations require bespoke code for each LLM platform and lack the composability of MCP's tool ecosystem.
elasticsearch query composition and execution
Medium confidenceConstructs and executes Elasticsearch queries through Kibana's query DSL interface, translating natural language or structured parameters into Elasticsearch Query Language (EQL) or JSON query syntax. Handles index selection, field mapping, aggregation setup, and result formatting to enable LLMs to perform complex searches without manual query syntax knowledge.
Bridges natural language query intent to Elasticsearch DSL through Kibana's query abstraction, allowing LLMs to construct valid queries without deep Elasticsearch syntax knowledge. Leverages Kibana's index pattern metadata to infer field types and valid operators.
Abstracts Elasticsearch query complexity through Kibana's UI-driven query builder, whereas direct Elasticsearch clients require LLMs to generate raw DSL syntax, increasing error rates and requiring more context about cluster schema.
dashboard and visualization metadata retrieval
Medium confidenceFetches metadata about saved Kibana dashboards, visualizations, and saved searches, including panel definitions, data sources, and configuration. Enables LLMs to discover available dashboards, understand their structure, and reference them in conversations without requiring manual documentation or UI navigation.
Exposes Kibana's saved objects API through MCP tools, allowing LLMs to introspect dashboard structure and discover available visualizations without UI navigation. Caches metadata in MCP context to reduce repeated API calls.
Provides programmatic access to dashboard metadata through MCP, whereas manual Kibana UI navigation requires human interaction and doesn't integrate with LLM workflows; direct Elasticsearch access lacks Kibana's abstraction of saved objects.
index pattern and field mapping discovery
Medium confidenceRetrieves Elasticsearch index pattern configurations and field mappings from Kibana, exposing field names, data types, and aggregation capabilities. Enables LLMs to understand the schema of available indices and construct valid queries without requiring external schema documentation or trial-and-error field exploration.
Exposes Kibana's index pattern API to provide schema-aware field discovery, allowing LLMs to understand Elasticsearch field types and constraints without manual schema documentation. Integrates field metadata into MCP tool context for query construction.
Provides schema discovery through Kibana's abstraction layer, whereas direct Elasticsearch mapping APIs require parsing raw JSON and lack Kibana's field formatting and UI-friendly metadata; manual documentation is error-prone and requires constant updates.
alert and anomaly detection configuration
Medium confidenceManages Kibana alerting rules and anomaly detection jobs, allowing LLMs to create, modify, and query alert configurations. Supports threshold-based alerts, anomaly detection rules, and integration with notification channels (email, Slack, webhooks) to enable automated incident response workflows triggered by observability data.
Exposes Kibana's alerting and anomaly detection APIs through MCP, enabling LLMs to programmatically create and manage alerts without UI interaction. Integrates with Kibana's action connectors to support multi-channel notifications.
Provides alert management through Kibana's native alerting framework, whereas custom alert systems require building separate infrastructure; direct Elasticsearch monitoring lacks Kibana's UI-driven rule builder and action connector ecosystem.
apm and distributed tracing data retrieval
Medium confidenceQueries Elastic APM (Application Performance Monitoring) data through Kibana, retrieving transaction traces, service metrics, and error information. Supports filtering by service, transaction type, and time range to enable LLMs to analyze application performance and troubleshoot latency or error issues without manual APM UI navigation.
Integrates Kibana's APM app API to expose distributed tracing data through MCP, allowing LLMs to analyze transaction traces and service dependencies without manual APM UI interaction. Supports trace-level filtering and span aggregation.
Provides APM data access through Kibana's abstraction, whereas direct Elasticsearch queries require knowledge of APM index structure and span schema; manual APM UI navigation doesn't integrate with LLM workflows.
log aggregation and pattern analysis
Medium confidenceAggregates logs from Elasticsearch indices and performs pattern analysis (e.g., identifying common error messages, grouping by log level). Enables LLMs to summarize log data, identify trends, and extract actionable insights without requiring manual log parsing or regex pattern matching.
Leverages Kibana's aggregation framework to perform log pattern analysis, exposing common error messages and log trends through MCP without requiring LLMs to parse raw log text. Integrates with Elasticsearch's terms and significant_terms aggregations.
Provides structured log analysis through Kibana's aggregation API, whereas manual log parsing requires regex or NLP; direct Elasticsearch queries require understanding aggregation syntax and field mappings.
metrics and time-series data visualization
Medium confidenceRetrieves time-series metrics (CPU, memory, network, application-specific metrics) from Elasticsearch and formats them for visualization or analysis. Supports metric aggregation, downsampling, and time-window bucketing to enable LLMs to analyze infrastructure and application performance trends without manual metric query construction.
Exposes Kibana's metrics aggregation and visualization APIs through MCP, enabling LLMs to query time-series data with automatic bucketing and downsampling. Supports multi-metric comparisons and dimension-based filtering.
Provides time-series metric access through Kibana's abstraction, whereas direct Elasticsearch queries require manual date histogram and aggregation setup; manual metric UI navigation doesn't integrate with LLM workflows.
mcp tool schema generation and dynamic exposure
Medium confidenceDynamically generates MCP tool schemas for Kibana API endpoints and exposes them to Claude and other MCP clients. Implements schema inference from Kibana's OpenAPI/Swagger definitions or hardcoded endpoint specifications, enabling clients to discover available tools and their parameters without external documentation.
Implements MCP tool schema generation for Kibana endpoints, allowing dynamic exposure of API operations to Claude without manual schema definition. Uses MCP's standard tool protocol to enable seamless integration with MCP-compatible clients.
Provides standardized MCP tool exposure for Kibana, whereas custom integrations require bespoke schema definition for each LLM platform; manual schema maintenance is error-prone and doesn't scale across multiple endpoints.
authentication and credential management
Medium confidenceManages authentication to Kibana instances using API keys, basic auth, or token-based authentication. Implements secure credential storage and request signing to enable the MCP server to authenticate with Kibana on behalf of clients without exposing credentials to LLM clients.
Implements server-side credential management for Kibana authentication, allowing the MCP server to handle authentication without exposing credentials to LLM clients. Supports multiple authentication methods and secure request signing.
Centralizes Kibana authentication in the MCP server, whereas client-side authentication requires distributing credentials to each LLM client and increases security risk; manual credential management is error-prone and doesn't scale.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓DevOps engineers building LLM-powered observability assistants
- ✓Teams integrating Kibana with Claude for log analysis and incident response
- ✓Developers automating Elastic Stack operations through conversational interfaces
- ✓SREs and on-call engineers using Claude for rapid log analysis during incidents
- ✓Data analysts building LLM-powered dashboards that query Elasticsearch dynamically
- ✓Observability teams automating root-cause analysis workflows
- ✓On-call engineers unfamiliar with the observability stack who need to discover relevant dashboards
- ✓LLM agents building dynamic reports by composing data from multiple saved visualizations
Known Limitations
- ⚠No built-in caching of Kibana responses — each query hits the live API, adding latency for repeated queries
- ⚠Requires direct network access to Kibana instance; no support for proxying through firewalls or VPNs without external configuration
- ⚠Limited to Kibana API surface area — cannot access features only available in Kibana UI (e.g., certain visualization interactions)
- ⚠No request rate limiting or throttling — high-volume queries could overwhelm Kibana or trigger rate limits
- ⚠Query complexity is limited by Kibana's abstraction layer — advanced Elasticsearch features (e.g., custom scoring, complex nested aggregations) may not be fully exposed
- ⚠No query optimization or cost estimation — large time ranges or high-cardinality fields can trigger expensive queries
Requirements
Input / Output
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Kibana MCP Server
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