{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm_npm-tocharianoumcp-server-kibana","slug":"npm-tocharianoumcp-server-kibana","name":"@tocharianou/mcp-server-kibana","type":"mcp","url":"https://www.npmjs.com/package/@tocharianou/mcp-server-kibana","page_url":"https://unfragile.ai/npm-tocharianoumcp-server-kibana","categories":["mcp-servers"],"tags":["kibana","mcp","mcp-server","elasticsearch","model-context-protocol","elastic-stack","observability","monitoring","dashboard","visualization","analytics","logs","metrics","apm","claude","anthropic","api-management","openapi","devops","elastic"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm_npm-tocharianoumcp-server-kibana__cap_0","uri":"capability://tool.use.integration.kibana.api.query.execution.via.mcp.protocol","name":"kibana api query execution via mcp protocol","description":"Executes 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.","intents":["Query Kibana dashboards and visualizations programmatically from Claude without manual API calls","Retrieve saved searches, index patterns, and analytics data directly within an LLM conversation","Automate Kibana operations like creating alerts or modifying dashboards through natural language instructions"],"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"],"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"],"requires":["Kibana 7.10+ instance with accessible REST API endpoint","Valid Kibana API key or basic authentication credentials","Node.js 16+ runtime for MCP server","Network connectivity from MCP server to Kibana instance (HTTP/HTTPS)"],"input_types":["MCP tool call with Kibana API endpoint path","HTTP method (GET, POST, PUT, DELETE)","JSON request body for POST/PUT operations","Query parameters for filtering/pagination"],"output_types":["JSON response from Kibana API","Structured dashboard/visualization metadata","Search results and aggregation data","Error messages with HTTP status codes"],"categories":["tool-use-integration","api-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-tocharianoumcp-server-kibana__cap_1","uri":"capability://data.processing.analysis.elasticsearch.query.composition.and.execution","name":"elasticsearch query composition and execution","description":"Constructs 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.","intents":["Search logs and metrics across Elasticsearch indices using natural language filters","Perform aggregations (counts, percentiles, time-series) on observability data without writing raw Elasticsearch queries","Retrieve time-range-bounded data for incident analysis or trend detection"],"best_for":["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"],"limitations":["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","Index schema must be pre-configured in Kibana; dynamic index discovery is not supported","Aggregation results are limited by Kibana's default bucket sizes and pagination — very large result sets may be truncated"],"requires":["Elasticsearch cluster 7.10+ with Kibana 7.10+ as query interface","Index patterns configured in Kibana with field mappings","Appropriate Kibana role/permissions for querying target indices","Knowledge of target index names and field names for query construction"],"input_types":["Natural language query description","Structured filter parameters (field, operator, value)","Time range specifications (relative or absolute)","Aggregation type and field names"],"output_types":["Elasticsearch query DSL JSON","Aggregation results with bucketing","Hit counts and sample documents","Time-series data for visualization"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-tocharianoumcp-server-kibana__cap_2","uri":"capability://memory.knowledge.dashboard.and.visualization.metadata.retrieval","name":"dashboard and visualization metadata retrieval","description":"Fetches 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.","intents":["List available dashboards and visualizations to understand what observability data is tracked","Retrieve dashboard panel definitions to understand what metrics are being monitored","Find saved searches related to specific services or components for targeted analysis"],"best_for":["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","Teams documenting their observability setup through Claude-powered queries"],"limitations":["Metadata retrieval does not include actual data — only dashboard structure and configuration","Large numbers of dashboards (100+) may cause slow enumeration and high memory usage in the MCP server","Dashboard descriptions and titles are user-defined; no automatic semantic indexing for discovery","Visualization definitions are Kibana-specific; exporting or migrating dashboard metadata to other tools requires additional parsing"],"requires":["Kibana instance with saved dashboards and visualizations","Read permissions on Kibana saved objects (dashboards, visualizations, searches)","API key or credentials with access to .kibana system index"],"input_types":["Dashboard ID or name filter","Visualization type filter (line, bar, pie, etc.)","Search scope (all spaces or specific space)"],"output_types":["JSON metadata for dashboards (title, description, panels)","Visualization definitions (type, data source, aggregations)","Saved search configurations","Index pattern references"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-tocharianoumcp-server-kibana__cap_3","uri":"capability://memory.knowledge.index.pattern.and.field.mapping.discovery","name":"index pattern and field mapping discovery","description":"Retrieves 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.","intents":["Discover available fields in a log index to construct targeted search queries","Understand field data types (keyword, numeric, date) to apply appropriate filters and aggregations","Identify which fields support full-text search vs. exact matching for query optimization"],"best_for":["LLM agents that need to dynamically construct queries based on available schema","Teams onboarding new engineers who need to understand observability data structure","Automated systems generating Kibana queries from natural language without hardcoded field knowledge"],"limitations":["Field mappings are static snapshots — changes to index mappings require manual refresh or server restart","High-cardinality fields (e.g., user IDs, request IDs) may have incomplete or sampled field lists in Kibana","Custom field formatters and scripted fields are not fully exposed through the API","Index pattern aliases and multi-index patterns may cause confusion in field discovery"],"requires":["Kibana index patterns configured for target Elasticsearch indices","Read access to Kibana saved objects and index pattern definitions","Elasticsearch cluster with accessible field mappings"],"input_types":["Index pattern name or ID","Field name filter or search term"],"output_types":["Field list with data types (keyword, text, numeric, date, etc.)","Field descriptions and custom labels","Aggregation support indicators (aggregatable, searchable)","Field format specifications"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-tocharianoumcp-server-kibana__cap_4","uri":"capability://automation.workflow.alert.and.anomaly.detection.configuration","name":"alert and anomaly detection configuration","description":"Manages 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.","intents":["Create new alerts based on log patterns or metric thresholds discovered during analysis","Modify existing alert rules to adjust sensitivity or notification targets","Query alert history and status to understand recent incidents and alert fatigue"],"best_for":["DevOps teams automating alert creation based on incident patterns","SREs building self-healing systems that adjust alerting rules based on observed noise","Incident response teams using Claude to rapidly create alerts for emerging issues"],"limitations":["Alert creation requires understanding Kibana's alerting rule syntax and action connectors — complex rules may fail silently","No built-in validation of alert conditions before creation — invalid rules may not trigger until first evaluation","Notification channel configuration (Slack, email, webhooks) must be pre-configured in Kibana; MCP server cannot create new connectors","Alert history and execution logs have retention limits in Kibana; old alerts may not be queryable"],"requires":["Kibana 7.10+ with alerting and actions enabled","Appropriate Kibana role with alert management permissions","Pre-configured notification connectors (email, Slack, webhooks, etc.)","Valid Elasticsearch queries for alert conditions"],"input_types":["Alert name and description","Query or anomaly detection rule definition","Threshold values and comparison operators","Notification channel and action configuration","Alert schedule (interval and time window)"],"output_types":["Alert rule ID and configuration","Alert execution history and status","Notification delivery logs","Anomaly detection job status and results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-tocharianoumcp-server-kibana__cap_5","uri":"capability://data.processing.analysis.apm.and.distributed.tracing.data.retrieval","name":"apm and distributed tracing data retrieval","description":"Queries 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.","intents":["Retrieve transaction traces for a specific service to identify performance bottlenecks","Query error rates and error details for a service during a specific time window","Analyze service dependencies and inter-service latency to understand distributed system behavior"],"best_for":["Application engineers debugging performance issues with Claude-powered analysis","SREs investigating service degradation by querying APM data programmatically","Teams building automated performance monitoring dashboards"],"limitations":["APM data retention is limited by Kibana configuration — old traces may be purged and unavailable","Trace sampling may cause missing or incomplete transaction data for high-volume services","Complex distributed traces with many spans may be truncated or slow to retrieve","Service names and transaction types must be known in advance; no automatic service discovery"],"requires":["Elastic APM agents deployed and reporting to Elasticsearch","Kibana 7.10+ with APM app enabled","Read access to APM indices (.apm-*)","Service names and transaction types configured in APM agents"],"input_types":["Service name","Transaction type (request, job, etc.)","Time range (relative or absolute)","Error filter (optional)","Trace ID or transaction ID (optional)"],"output_types":["Transaction traces with span details","Service metrics (throughput, latency, error rate)","Error details and stack traces","Service dependency graph"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-tocharianoumcp-server-kibana__cap_6","uri":"capability://data.processing.analysis.log.aggregation.and.pattern.analysis","name":"log aggregation and pattern analysis","description":"Aggregates 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.","intents":["Summarize error logs from the past hour to identify common failure patterns","Group logs by service and error message to understand which components are failing","Extract key information from unstructured logs (timestamps, error codes, affected users) for incident reports"],"best_for":["On-call engineers triaging incidents by analyzing log patterns with Claude","Teams building automated incident summaries from log data","DevOps engineers investigating deployment issues through log analysis"],"limitations":["Pattern analysis is limited to Kibana's aggregation capabilities — complex NLP or semantic analysis is not supported","Unstructured logs require pre-processing (parsing, field extraction) in Elasticsearch; raw text analysis is limited","Large log volumes (millions of events) may cause slow aggregations or timeout","Log retention policies may limit historical analysis to recent time windows"],"requires":["Elasticsearch indices with log data (e.g., from Filebeat, Logstash)","Kibana index patterns configured for log indices","Appropriate field mappings for log level, timestamp, and message fields"],"input_types":["Log index name or pattern","Time range for analysis","Filter criteria (service, log level, error code, etc.)","Aggregation field (e.g., error message, service name)"],"output_types":["Aggregated log counts by category","Top error messages and frequencies","Log samples and excerpts","Time-series log volume trends"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-tocharianoumcp-server-kibana__cap_7","uri":"capability://data.processing.analysis.metrics.and.time.series.data.visualization","name":"metrics and time-series data visualization","description":"Retrieves 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.","intents":["Retrieve CPU and memory metrics for a server over the past 24 hours to identify resource constraints","Query application-specific metrics (request latency, throughput) to understand performance trends","Compare metrics across multiple services or time periods to identify anomalies or improvements"],"best_for":["Infrastructure engineers analyzing system performance with Claude","SREs building automated capacity planning reports from metric data","Teams investigating performance regressions by comparing metric trends"],"limitations":["Metric resolution is limited by collection interval — high-frequency metrics may be downsampled or unavailable","Custom metrics require pre-configuration in metric collection agents; ad-hoc metric creation is not supported","Metric retention policies may limit historical analysis to recent time windows (e.g., 30 days)","Cross-metric correlation analysis is limited to Kibana's built-in visualization capabilities"],"requires":["Elasticsearch indices with metric data (e.g., from Metricbeat, Prometheus, custom agents)","Kibana index patterns configured for metric indices","Metric field names and aggregation types defined in index mappings"],"input_types":["Metric name or index pattern","Time range for analysis","Filter criteria (host, service, environment, etc.)","Aggregation type (average, max, min, percentile, etc.)","Time bucket size (1m, 5m, 1h, etc.)"],"output_types":["Time-series metric data with timestamps","Aggregated metric values (average, percentile, etc.)","Metric comparison across dimensions","Anomaly detection results (if enabled)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-tocharianoumcp-server-kibana__cap_8","uri":"capability://tool.use.integration.mcp.tool.schema.generation.and.dynamic.exposure","name":"mcp tool schema generation and dynamic exposure","description":"Dynamically 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.","intents":["Automatically expose Kibana API endpoints as MCP tools without manual schema definition","Enable Claude to discover available Kibana operations and their required parameters","Support dynamic tool registration as new Kibana features or custom endpoints are added"],"best_for":["MCP server developers building integrations with Kibana or other Elastic Stack components","Teams deploying MCP servers that need to expose multiple API endpoints with minimal configuration","Developers extending MCP servers with custom Kibana operations"],"limitations":["Schema generation is limited to Kibana's documented API endpoints — undocumented or internal APIs are not exposed","Complex nested parameters or polymorphic request bodies may not be accurately represented in MCP schemas","Schema updates require server restart or manual refresh — dynamic schema changes are not supported","Tool discovery is limited to MCP's tool listing mechanism; no full-text search or semantic discovery"],"requires":["Node.js 16+ with MCP SDK","Kibana instance with accessible REST API","MCP client (Claude, other LLM) with tool calling support"],"input_types":["Kibana API endpoint path","HTTP method (GET, POST, PUT, DELETE)","Request parameters and body schema"],"output_types":["MCP tool schema (JSON)","Tool name and description","Parameter definitions with types and constraints"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-tocharianoumcp-server-kibana__cap_9","uri":"capability://safety.moderation.authentication.and.credential.management","name":"authentication and credential management","description":"Manages 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.","intents":["Securely authenticate to Kibana without requiring clients to provide credentials","Support multiple authentication methods (API key, basic auth, OAuth tokens) for different deployment scenarios","Rotate or update Kibana credentials without restarting the MCP server"],"best_for":["Teams deploying MCP servers in shared environments where credential isolation is critical","Organizations with strict security policies requiring centralized credential management","Multi-tenant deployments where different clients need access to different Kibana instances"],"limitations":["Credentials are stored in environment variables or configuration files — no built-in secrets management integration (e.g., HashiCorp Vault, AWS Secrets Manager)","No support for dynamic credential rotation or expiration — credentials must be manually updated","Authentication is server-to-Kibana only; 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