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langfuse_get_trace

Retrieve detailed trace data including all observations from Langfuse to debug AI agent sessions, analyze LLM usage patterns, and track performance metrics.

Instructions

[Langfuse] Get a specific trace with its observations. Returns full trace data including all observations (generations, spans, events).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
trace_idYesThe trace ID to fetch
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states it returns full trace data including observations, but doesn't mention authentication requirements, rate limits, error conditions, or whether this is a read-only operation. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose and includes important details about what's returned. Every word serves a purpose with zero waste or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read operation with one parameter and no output schema, the description provides adequate context about what the tool does and returns. However, without annotations covering behavioral aspects like authentication or safety, and no output schema to describe the return format, there are completeness gaps that could hinder effective tool selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100% with one parameter clearly documented, so the baseline is 3. The description doesn't add any parameter-specific information beyond what's in the schema, but doesn't need to compensate for coverage gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get a specific trace') and resource ('trace with its observations'), specifying it returns full trace data including all observations. It distinguishes from siblings like 'langfuse_list_traces' by focusing on a single trace, but doesn't explicitly contrast with 'langfuse_get_session' or 'langfuse_get_observation'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when you need a specific trace with its observations, but doesn't provide explicit guidance on when to use this versus alternatives like 'langfuse_list_traces' for multiple traces or 'langfuse_get_observation' for individual observations. No exclusions or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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