Skip to main content
Glama
avivsinai

langfuse-mcp

get_dataset_item

Retrieve a complete dataset item by its ID to access input, expected output, metadata, and linked traces for analysis.

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
Behavior4/5

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

Without annotations, the description details the return fields (input, expectedOutput, etc.), implying a read-only retrieval. However, it does not explicitly state read-only behavior or prerequisites. The structured return info compensates somewhat.

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 concise with front-loaded purpose, followed by a list of parameters and returns. No redundant sentences; every line adds value.

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?

With a simple 2-parameter tool and an output schema referenced (though not shown), the description covers all necessary aspects: purpose, parameters, and return format in detail. It is sufficient for correct tool invocation.

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?

Schema coverage is 100%, baseline 3. The description adds context for 'output_mode' by explaining the effect of each enum value (compact truncates, full_json_string returns full data), which is beyond the schema description. 'item_id' is clearly explained.

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 'Get a specific dataset item by ID', providing a specific verb and resource. It distinguishes from sibling tools like 'get_dataset' or 'list_dataset_items' by focusing on a single item retrieval.

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 implicitly indicates usage for fetching a single known item via ID, but does not explicitly compare with alternatives or state when not to use it. Sibling tools exist that list items, but no exclusion is provided.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/avivsinai/landfuse-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server