Elasticsearch
This Elasticsearch MCP server enables AI-powered log analysis by connecting Claude Desktop directly to Elasticsearch clusters, allowing users to query logs using natural language instead of complex Elasticsearch syntax. Built with Python and the FastMCP framework, it provides five specialized tools for searching logs, analyzing error patterns with root cause analysis, detecting performance bottlenecks, monitoring cluster health, and optimizing index performance. The implementation features intelligent time range parsing, automatic error signature extraction, performance trend analysis, and generates actionable recommendations for system optimization, making it ideal for DevOps teams and SRE engineers who need to quickly diagnose issues and gain insights from their log data without writing complex queries.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-02.
Scan details: Circle-IR · 2026-09-02 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- devops-ci
- Source
- PulseMCP
- Repository
- github.com/y0zg/mcp-elasticsearch
- Author type
- human
- Last scanned
- 2026-09-02
- Updated
- 2026-09-02
Use via MCP
Resolve Elasticsearch from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.