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avivsinai

langfuse-mcp

get_dataset

Retrieve a specific dataset by name from Langfuse to access its metadata, item count, and associated runs for LLM application observability.

Instructions

Get a specific dataset by name.

Retrieves dataset details including metadata and item count.

Args:
    ctx: Context object containing lifespan context with Langfuse client
    name: The name of the dataset to fetch

Returns:
    A dictionary containing dataset details:
    - id: Unique dataset identifier
    - name: Dataset name
    - description: Dataset description
    - metadata: Custom metadata
    - items: List of dataset items (if included by the API)
    - runs: List of dataset runs (if included by the API)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe name of the dataset to fetch

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses this is a retrieval operation ('get', 'retrieves') and describes return content, but lacks behavioral details like error handling, authentication needs, rate limits, or whether it's read-only. The description adds some context but misses key operational 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 clear sections (purpose, Args, Returns) and front-loaded purpose. However, the Args section repeats schema info unnecessarily, and the Returns section could be more concise given the output schema exists. Overall efficient but with minor redundancy.

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?

Given one parameter with full schema coverage and an output schema, the description provides adequate context for a simple retrieval tool. It explains what the tool does and what it returns, though behavioral transparency could be improved. For this complexity level, it's reasonably complete.

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%, with the schema fully documenting the 'name' parameter. The description repeats the parameter info in the Args section but adds no additional meaning beyond what the schema provides. Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'get' and resource 'dataset by name', specifying it retrieves dataset details including metadata and item count. It distinguishes from siblings like 'list_datasets' (which lists multiple) and 'get_dataset_item' (which gets individual items), though not explicitly named. Purpose is specific but sibling differentiation is implicit rather than explicit.

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 when needing details for a specific dataset by name, but provides no explicit guidance on when to use this versus alternatives like 'list_datasets' or 'get_dataset_item'. No exclusions or prerequisites are mentioned, leaving usage context somewhat vague.

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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