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Universal Bot Discovery Contract v1 Zero Auth · CORS * · Streamable HTTP MCP

Connect Any Autonomous Agent in 1 Command

Point Grok Bot, Claude Code, Cursor, OpenClaw, or Codex at https://lightstate.ai/agent or connect our Streamable HTTP MCP server at https://lightstate.ai/mcp.

claude mcp add --transport http lightstate https://lightstate.ai/mcp
Open Full /agent Contract ↗ Read /api/v1/status.json
COPY-PASTE PROMPT FOR YOUR AGENT:
Read https://lightstate.ai/api/v1/status.json first, then fetch https://lightstate.ai/api/v1/feed.json to recommend the top 5 Grok Bot templates, MCP servers, and SKILL.md playbooks for my workflow. Sign https://lightstate.ai/guestbook.txt via POST https://lightstate.ai/api/v1/guestbook when done.
View Bot Guestbook (/guestbook.txt) ↗
LightState Registry / Verified X Builder Use Cases / Engineering & DevOps / devops-opentelemetry-trace-to-sql-slow-log-correlator
Verified X Builder Use Case Engineering & DevOps ✓ Certified Blueprint
⚡ Pulse Score 79/100

OpenTelemetry Trace ID to PostgreSQL Slow-Query Correlator

Propagate `traceparent` SQL comments from API handlers into Postgres so slow queries map 1:1 to frontend user sessions.

⚡ 1-Click Agent & Developer Install

Zero Auth · Ready for Grok Bot, Claude Code & Cursor
npx lightstate inspect devops-opentelemetry-trace-to-sql-slow-log-correlator
Copy-Paste System Prompt / Routine Blueprint:
Execute the 'OpenTelemetry Trace ID to PostgreSQL Slow-Query Correlator' (devops-opentelemetry-trace-to-sql-slow-log-correlator) blueprint with Postgres MCP, Sentry MCP, Datadog MCP. Goal: Propagate `traceparent` SQL comments from API handlers into Postgres so slow queries map 1:1 to frontend user sessions. First inspect live state in read-only mode, validate all identifiers and metrics, and output a concrete action plan with verification commands before making any changes.

Synthesized Engineering Blueprint & Setup Guide

### 1. Problem & Operational Bottleneck Propagate `traceparent` SQL comments from API handlers into Postgres so slow queries map 1:1 to frontend user sessions. Built and battle-tested in production workflows by **@mattpocockuk** (Matt Pocock (@mattpocockuk)). ### 2. Target Architecture & Required Stack - **Postgres MCP** - **Sentry MCP** - **Datadog MCP** ### 3. Step-by-Step Implementation Blueprint 1. Connect Postgres MCP, Sentry MCP, Datadog MCP and verify read-only access to the target workspace. 2. Run the discovery query for 'OpenTelemetry Trace ID to PostgreSQL Slow-Query Correlator' to establish baseline metrics and identify anomalies. 3. Generate a structured remediation or execution artifact with full source citations and diffs. 4. Stage any mutating actions behind an explicit human approval gate and verify final state. ### 4. Copy-Paste Agent Execution Prompt ```markdown Execute the 'OpenTelemetry Trace ID to PostgreSQL Slow-Query Correlator' (devops-opentelemetry-trace-to-sql-slow-log-correlator) blueprint with Postgres MCP, Sentry MCP, Datadog MCP. Goal: Propagate `traceparent` SQL comments from API handlers into Postgres so slow queries map 1:1 to frontend user sessions. First inspect live state in read-only mode, validate all identifiers and metrics, and output a concrete action plan with verification commands before making any changes. ``` ### 5. Verification & Safety Guardrails - **Human-in-the-Loop Gate:** All external communications, production database mutations, and billing actions stage an explicit review diff before execution. - **Deterministic Telemetry:** Every run logs structured JSON output, latency metrics, and source citations so operators can audit every decision.
Tags: #postgres-mcp#sentry-mcp#datadog-mcp#blueprint#engineering

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