TelemetryFlow's Python MCP server implementation provides direct access to observability and telemetry data through a clean, domain-driven architecture. Built using CQRS patterns and structured around session management, tool execution, and conversation handling, it offers built-in tools for file operations, system information, and code analysis. The server includes comprehensive telemetry collection through the TelemetryFlow SDK, supports multiple storage backends (PostgreSQL, ClickHouse, Redis), and provides built-in prompts for code review and debugging tasks.
Cognium trust score
55%
Tier
Scanned
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.
Returns 7 tools: search_skills, get_skill, list_leaderboard, get_trust_breakdown, resolve_composition, plus the ChatGPT-connector search and fetch. Every tool is annotated read-only.
Resolve this skill directly via MCP tools/call get_skill.