Connect Your AI Agent with Logfire for Slack

Executes an arbitrary SQL query against Logfire's records (unified logs + traces, one row per span) or metrics tables and returns the results. Use this for any analysis, search, or aggregation over your telemetry — recent activity, error hunting, latency analysis, counts, etc. To discover what columns are available before writing a query, run SELECT column_name, data_type FROM information_schema.columns WHERE table_name = 'records' (or 'metrics') with this same tool — there is no separate schema-discovery tool. Useful columns on records include start_timestamp, span_name, message, level, service_name, duration, is_exception, exception_type, and exception_message. Example — find exceptions: SELECT span_name, exception_type, exception_message FROM records WHERE is_exception = true ORDER BY start_timestamp DESC. Example — count by level: SELECT level, count(*) AS n FROM records GROUP BY level ORDER BY n DESC. After using Record Log Entry to write data, use this tool to confirm what was recorded. See the SQL reference and query API docs.

Records a single log entry or event in Logfire as a zero-duration span, via OTLP ingestion. Use this whenever the user wants to log, record, or note an event, deployment, incident, or error — this is Logfire's equivalent of calling logfire.info()/logfire.error() from the SDK. This creates a single point-in-time entry, not a full distributed trace with parent/child spans. Setting exceptionType/exceptionMessage marks the entry as an exception (is_exception = true when later queried). After recording, use Run SQL Query to confirm what was written (e.g. SELECT * FROM records WHERE message LIKE '%...%' ORDER BY start_timestamp DESC LIMIT 1). See the documentation