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"How to write a query in Elasticsearch" matching MCP servers:

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    Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.
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    MIT
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    Enables querying and searching logs from Kibana (ELK stack) via MCP, with support for project-based data views, trace ID correlation, and log expansion.
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    An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.
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    MIT
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    MCP server for Kibana / Elasticsearch — log search, aggregations, index discovery, and dashboard browsing. Hits Elasticsearch REST API directly for log queries; falls back to Kibana Console proxy when no direct ES URL is configured. Supports ApiKey auth (best for agents), Basic auth, and anonymous access. All 5 tools are read-only (readOnlyHint: true). Returns structured JSON (outputSchema).
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    MIT
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    Enables LLM agents to search and analyze Elasticsearch logs for errors, detect recurring patterns, analyze error-rate trends, and retrieve full trace context through MCP tools.
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    MIT
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    Enables 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.
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    MIT
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    A comprehensive read-only MCP server for Datadog, providing 117 tools to query logs, APM, metrics, monitors, dashboards, SLOs, and more, with zero write capabilities for safe AI integration.
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    MIT
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    Integrates AI assistants with Graylog to query and analyze log data using Elasticsearch syntax and stream-specific filtering. It enables users to perform advanced searches, retrieve log statistics, and manage Graylog streams through natural language.
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    MIT
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    Enables AI assistants to interact with self-hosted Dynatrace Managed environments to retrieve observability data, security insights, and performance metrics. It allows users to query problems, logs, events, and SLOs through natural language interfaces in both local and remote modes.
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    Apache 2.0
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    A read-only MCP server for the Auralogs logging platform, enabling AI agents to query production logs, search errors, and retrieve AI analyses without write access. It allows you to ask your coding assistant 'what's broken in production right now?' by connecting to your logs via a simple bearer auth key.
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    MIT
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    An MCP server that connects Claude (or any MCP compatible client) to your existing log infrastructure. Query, summarize, and trace logs in plain English across GCP Cloud Logging, AWS CloudWatch, Azure Log Analytics, Grafana Loki, and Elasticsearch without writing filter expressions or leaving your editor.
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    Small production-oriented MCP server for diagnosing incidents from Elasticsearch logs with unknown schema. It provides tools for log discovery, retrieval, and issue diagnosis.
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    MIT
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    Enables AI assistants to query Prometheus metrics, monitor alerts, and analyze system health through read-only access to your Prometheus server with built-in query safety and optional AI-powered metric analysis.
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    MIT