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avivsinai

langfuse-mcp

get_dataset_item

Retrieve a specific dataset item by ID to access input, expected output, metadata, and linked trace data for LLM application analysis and debugging.

Instructions

Get a specific dataset item by ID.

Retrieves the full dataset item including input, expected output, metadata, and linked traces.

Args:
    ctx: Context object containing lifespan context with Langfuse client
    item_id: The ID of the dataset item to fetch
    output_mode: How to format the response data

Returns:
    A dictionary containing the dataset item details:
    - id: Unique item identifier
    - datasetId: Parent dataset ID
    - input: Input data for the item
    - expectedOutput: Expected output data
    - metadata: Custom metadata
    - sourceTraceId: Linked trace ID (if any)
    - sourceObservationId: Linked observation ID (if any)
    - status: Item status (ACTIVE or ARCHIVED)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
item_idYesThe ID of the dataset item to fetch
output_modeNoOutput format: 'compact' truncates, 'full_json_string' returns full data, 'full_json_file' writes to filecompact

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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 effectively describes the read-only nature ('Retrieves'), the data returned, and the output formatting options. However, it doesn't mention potential error conditions, authentication requirements, rate limits, or whether the operation is idempotent, which would be valuable for a tool with no annotation coverage.

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

Conciseness4/5

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

The description is well-structured with clear sections (purpose, retrieval details, Args, Returns) and front-loads the core purpose. However, the 'Args' and 'Returns' sections are somewhat redundant with the schema and output schema, making the description slightly longer than necessary while still being efficient overall.

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

Completeness5/5

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

Given the tool's moderate complexity (2 parameters, 1 required), 100% schema coverage, and the presence of an output schema (implied by the detailed 'Returns' section), the description is complete enough. It covers the purpose, parameters, return values, and distinguishes the tool from siblings, providing all necessary context for an agent to use it correctly without being overly verbose.

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

Parameters4/5

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

The schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds marginal value by briefly mentioning the parameters in the 'Args' section and providing context for 'output_mode' in the 'Returns' section, but doesn't significantly enhance understanding beyond what the schema provides. The baseline of 3 is appropriate, with a slight bump for the additional context about response formatting.

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 specific action ('Get a specific dataset item by ID') and resource ('dataset item'), distinguishing it from sibling tools like 'list_dataset_items' (which lists multiple items) and 'get_dataset' (which gets the dataset itself rather than individual items). The verb 'Get' combined with the specific resource type provides unambiguous purpose.

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?

The description implies usage context by specifying 'by ID' and mentioning what data is retrieved, which helps differentiate from 'list_dataset_items' for bulk operations. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_dataset' or provide exclusion criteria, leaving some ambiguity about the exact selection context.

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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