Use for Pydantic Logfire observability, tracing, and debugging. Query exceptions, spans, logs with SQL. View traces in Logfire UI. 4 tools for application monitoring and error analysis.
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Use for Pydantic Logfire observability, tracing, and debugging. Query exceptions, spans, logs with SQL. View traces in Logfire UI. 4 tools for application monitoring and error analysis.
version
1.0.0
title
Logfire
status
active
logfire Skill
Pydantic Logfire observability and tracing. Query application logs, find exceptions, analyze spans with SQL, and generate trace links.
Context Efficiency
Traditional MCP approach:
All 4 tools loaded at startup
Estimated context: 2000 tokens
This skill approach:
Metadata only: ~100 tokens
Full instructions (when used): ~5k tokens
Tool execution: 0 tokens (runs externally)
How This Works
Instead of loading all MCP tool definitions upfront, this skill:
Tells you what tools are available (just names and brief descriptions)
You decide which tool to call based on the user's request
Generate a JSON command to invoke the tool
The executor handles the actual MCP communication
Available Tools
find_exceptions_in_file: Get the details about the 10 most recent exceptions on the file.
arbitrary_query: Run an arbitrary query on the Pydantic Logfire database.
The SQL reference is available via the sql_reference tool.
logfire_link: Creates a link to help the user to view the trace in the Logfire UI.
schema_reference: The database schema for the Logfire DataFusion database.
This includes all tables, columns, and their types as well as descriptions.
For example:
-- The records table contains spans and logs.CREATE TABLE records (
message TEXT, -- The message of the record
span_name TEXT, -- The name of the span, message is usually templated from this
trace_id TEXT, -- The trace ID, identifies a group of spans in a trace
exception_type TEXT, -- The type of the exception
exception_message TEXT, -- The message of the exception-- other columns...
);
The SQL syntax is similar to Postgres, although the query engine is actually Apache DataFusion.
To access nested JSON fields e.g. in the attributes column use the -> and ->> operators.
You may need to cast the result of these operators e.g. (attributes->'cost')::float + 10.
You should apply as much filtering as reasonable to reduce the amount of data queried.
Filters on start_timestamp, service_name, span_name, metric_name, trace_id are efficient.
Usage Pattern
When the user's request matches this skill's capabilities:
Step 1: Identify the right tool from the list above
