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langfuse_trace

Log LLM interactions into Langfuse traces to capture input, output, model, and metadata for observability.

Instructions

Create a Langfuse trace to log an LLM interaction (input, output, model, metadata).

Requires scope: integrations:observe:write. Every call governed by Arbiter constitutional policy and sealed with a ProofLink cryptographic receipt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesTrace name / operation label
tagsNoTrace tags
inputNoInput payload (any JSON value)
modelNoModel ID used (e.g. claude-sonnet-4-6)
outputNoOutput payload (any JSON value)
userIdNoEnd-user identifier
metadataNoArbitrary metadata object
Behavior4/5

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

With no annotations, the description discloses key behavioral traits: it's a write operation requiring 'integrations:observe:write' scope and it produces a ProofLink cryptographic receipt with governance. This goes beyond a bare 'create' statement, though it doesn't detail side effects, idempotency, or return format.

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?

Two sentences, front-loaded with the action and resource, no filler. The second sentence adds required context (scope, receipt) without bloat.

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?

The tool is a create operation but has no output schema and the description doesn't state what it returns (e.g., trace ID or receipt), leaving agents uncertain about how to reference the created trace. It also doesn't explain the ProofLink receipt's role or how it connects to sibling verify tools.

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?

All 7 parameters already have schema descriptions (100% coverage), so the description adds little beyond what's in the schema. It echoes 'input, output, model, metadata' but doesn't clarify relationships or provide additional usage nuances.

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

Purpose5/5

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

The description uses a specific verb+resource ('Create a Langfuse trace') and states the exact purpose ('to log an LLM interaction'), clearly distinguishing it from sibling tools like langfuse_health or other logging/ITSM tools.

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

Usage Guidelines4/5

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

Provides clear context for when to use (logging an LLM interaction) and even specifies the required scope. However, it doesn't explicitly name alternatives or exclusions, so it stops short of 5.

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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