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create_dataset_item

Create or update a dataset item in Langfuse, storing input and expected output as JSON strings. Use to add test cases or modify existing dataset entries.

Instructions

Create or upsert a dataset item. input/expected_output: JSON strings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYes
item_idNo
projectNo
metadataNo
dataset_nameYes
expected_outputNo
source_trace_idNo
source_observation_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations, the description carries the full burden. It does disclose the 'upsert' behavior, which is a useful behavioral trait, but it fails to explain how upsert works (e.g., which field acts as the key), what happens to existing items, or any side effects. It also omits error cases and return value details.

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 exceptionally concise, containing only two short sentences. Every part serves a purpose: the first states the core action, and the second clarifies parameter types. There is no redundancy or fluff.

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

Completeness1/5

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

This is a mutation tool with 8 parameters, 2 required, and no schema descriptions, no annotations, and no behavioral details. The description only covers a fraction of what an agent needs to invoke it correctly. It fails to mention required parameters, the meaning of fields like item_id or metadata, or any constraints, making it far from complete.

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

Parameters2/5

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

The schema has 0% description coverage for its 8 parameters, so the description must compensate. It does clarify that 'input/expected_output' are JSON strings, which adds meaning beyond the raw 'string' type. However, the remaining 6 parameters (item_id, project, metadata, dataset_name, source_trace_id, source_observation_id) are left completely unexplained, making this inadequate for an 8-parameter tool.

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 clearly states the action: 'Create or upsert a dataset item.' This is a specific verb+resource pairing that distinguishes it from sibling tools like 'create_dataset' (creates a dataset) and 'delete_dataset_item' (deletes an item). The addition of 'upsert' provides extra nuance about the tool's behavior.

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

Usage Guidelines2/5

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

No usage guidance is provided. The description does not explain when to use this tool versus alternatives, nor does it mention any conditions or prerequisites. There are many sibling tools for datasets and items, but no cross-references or context for selection.

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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