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avivsinai

langfuse-mcp

list_dataset_items

List dataset items with pagination and optional filtering by source trace or observation ID. Retrieve input, expected output, and metadata for each item.

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?

No annotations provided, so description carries full behavioral burden. It clearly describes a read-only operation (listing items) and specifies return format (dictionary with data and metadata). Does not mention side effects, which is appropriate. No contradictions.

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

Conciseness3/5

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

Description includes docstring-style Args and Returns sections, making it somewhat lengthy. First sentence is effective, but subsequent lines often echo schema information. Could be more concise without losing clarity.

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

Completeness4/5

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

With 6 parameters, full schema coverage, and an output schema, the description adequately covers tool behavior and return structure. It explains pagination, filtering, and output modes. Slightly lacking in details about return object contents, but output schema compensates.

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 coverage is 100%, so baseline is 3. Description repeats some schema descriptions (e.g., dataset_name, page) but adds minor context for output_mode (explains enum values). Does not significantly enhance parameter meaning beyond schema.

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 it lists dataset items with pagination and optional filtering, using specific verb 'List items in a dataset'. It distinguishes from sibling tools like 'list_datasets' (which lists datasets) and 'get_dataset_item' (single item).

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 mentions pagination and filtering but does not provide explicit guidance on when to use this tool versus alternatives like 'create_dataset_item' or 'get_dataset_item'. An agent must infer context from the tool's purpose.

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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