SigNoz
This MCP server provides AI assistants with comprehensive access to SigNoz observability data through a Flask-based implementation built by Doctor Droid that supports both HTTP and STDIO transport modes. The implementation offers six core tools including connection testing, dashboard management (listing and fetching detailed panel data), custom metrics querying with PromQL support, and standardized APM metrics collection (request rate, error rate, latency) with hardcoded builder query templates that match SigNoz's frontend behavior. Built with Docker containerization, configurable SSL verification, and intelligent timestamp handling that defaults to 3-hour lookbacks, it serves DevOps teams needing AI-powered observability analysis, SREs requiring automated dashboard data extraction and incident investigation, and organizations wanting to integrate SigNoz monitoring data into conversational workflows for performance analysis and troubleshooting.
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 2026-09-28.
Scan details: Circle-IR · 2026-09-28 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- cloud-infra
- Source
- PulseMCP
- Repository
- github.com/drdroidlab/signoz-mcp-server
- Author type
- human
- Last scanned
- 2026-09-28
- Updated
- 2026-09-28
Use via MCP
Resolve SigNoz 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.