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langfuse-mcp-java

create_annotation_queue

create_annotation_queue
Destructive

Create annotation queues for human review workflows in Langfuse, enabling structured evaluation of LLM application outputs with configurable scoring.

Instructions

Creates a new annotation queue for human review workflows. Returns the created queue with its assigned ID. name is required. description and scoreConfigId are optional.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesQueue name. Required.
descriptionYesOptional description of the queue's purpose.
scoreConfigIdYesOptional score config ID to associate with this queue.
Behavior4/5

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

Adds valuable return value information ('Returns the created queue with its assigned ID') not present in annotations or output schema. Annotations already cover safety profile (destructiveHint=true, readOnlyHint=false), so description appropriately focuses on workflow context and return structure without contradiction.

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?

Three tightly focused sentences covering purpose, return value, and parameters. Structure is front-loaded, though the third sentence contains the parameter requirement inaccuracy.

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

Completeness3/5

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

Adequate for a creation tool with good annotations and full schema coverage. Describes return values sufficiently given the lack of output schema, but the parameter requirement error leaves a critical gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Attempts to clarify required vs optional parameters ('name is required... are optional'), but this contradicts the input schema which marks all three parameters as required. With 100% schema coverage, baseline is 3, but the misinformation reduces the score.

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?

Clear specific verb (Creates), resource (annotation queue), and scope (for human review workflows). Distinguishes from sibling create_annotation_queue_item by specifying 'queue' vs 'item'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use versus alternatives (e.g., when to create a queue vs. adding items to existing queues). Only states what the tool does, not when to invoke it or prerequisites.

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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