Skip to main content
Glama
Log-LogN

langfuse-mcp-java

delete_dataset_run

delete_dataset_run
Destructive

Remove a dataset run and all associated run items permanently to clean up experiment runs you no longer need. This irreversible action requires both dataset name and run name.

Instructions

Deletes a dataset run and all its run items. This action is irreversible. Use this to clean up experiment runs you no longer need. Both datasetName and runName are required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetNameYesDataset name (exact match). Required.
runNameYesRun name to delete (exact match). Required.
Behavior4/5

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

Annotations declare destructiveHint=true and idempotentHint=false; the description reinforces this with 'irreversible'. Critically, it adds behavioral detail not in annotations: 'and all its run items' clarifies the cascade deletion scope, explaining what collateral data gets destroyed. Could add context about openWorldHint behavior (e.g., error if run not found).

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?

Four sentences respectively cover purpose, safety warning, usage context, and parameter requirements. Front-loaded with the core action, zero redundancy, and appropriately terse for a destructive operation. Every sentence earns its place.

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 the destructive nature and lack of output schema, the description adequately covers irreversibility and cascade behavior. Annotations cover safety profile. Minor gap: does not address openWorldHint implications (e.g., behavior when dataset/run doesn't exist) or return value structure.

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% with both parameters fully documented. The description states 'Both datasetName and runName are required,' which merely confirms the schema's required array and property descriptions without adding semantic depth about format or lookup behavior. With complete schema coverage, 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 explicitly states 'Deletes a dataset run and all its run items'—a specific verb (deletes) with clear resource (dataset run) and scope (including cascade to run items). This distinguishes it from sibling tools like delete_dataset_item (which targets individual items) or delete_model.

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?

Provides clear usage context ('Use this to clean up experiment runs you no longer need') and warns of consequences ('This action is irreversible'). However, it lacks explicit 'when not to use' guidance or alternative suggestions (e.g., no mention of using get_dataset_run first to verify existence).

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/Log-LogN/langfuse-mcp-java'

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