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avivsinai

langfuse-mcp

list_dataset_items

Retrieve dataset items with pagination and filtering options to access input, expected output, and metadata for analysis.

Instructions

List items in a dataset with pagination and optional filtering.

Returns dataset items with their input, expected output, and metadata.

Args:
    ctx: Context object containing lifespan context with Langfuse client
    dataset_name: The name of the dataset to list items from
    source_trace_id: Optional filter by source trace ID
    source_observation_id: Optional filter by source observation ID
    page: Page number for pagination (starts at 1)
    limit: Maximum items per page (max 100)
    output_mode: How to format the response data

Returns:
    A dictionary containing:
    - data: List of dataset item objects
    - metadata: Pagination info (page, limit, total, dataset_name)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_nameYesThe name of the dataset to list items from
source_trace_idNoFilter by source trace ID
source_observation_idNoFilter by source observation ID
pageNoPage number for pagination (starts at 1)
limitNoItems per page (max 100)
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?

With no annotations provided, the description carries full burden and does well: it discloses pagination behavior (page starts at 1, limit max 100), filtering options, and output format modes. It could improve by mentioning rate limits or authentication needs, but covers core behavioral traits.

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 a clear purpose statement, parameter summary, and return format. It's front-loaded but includes a detailed 'Args' and 'Returns' section that, while helpful, adds length. Every sentence earns its place.

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 complexity (6 parameters, pagination, filtering) and no annotations, the description is complete: it explains purpose, parameters, and return values. With an output schema present, it doesn't need to detail return values further, making it sufficient for agent use.

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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema, restating some parameter purposes (e.g., 'optional filtering') without new syntax or format details. Baseline 3 is appropriate.

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 verb ('List') and resource ('items in a dataset'), specifying it includes pagination and optional filtering. It distinguishes from siblings like 'get_dataset_item' (singular) and 'list_datasets' (datasets vs items).

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 for listing dataset items with filtering, but doesn't explicitly state when to use this vs alternatives like 'get_dataset_item' (for a single item) or 'list_datasets' (for datasets). No explicit 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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