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queue_entity_extraction

Queue a document for asynchronous entity extraction. Returns a job ID to track progress while processing runs in the background.

Instructions

Queue a document for background entity extraction. Extraction happens asynchronously without blocking. Use this to extract entities from documents without waiting for LLM processing. Returns job ID for tracking progress.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
doc_idYesDocument ID to extract entities from
confidence_thresholdNoMinimum confidence threshold (0.0-1.0, default: 0.6)
skip_if_existsNoSkip if entities already exist or job is queued (default: true)
Behavior4/5

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

Without annotations, the description carries full burden. It discloses key behavioral traits: asynchronous, non-blocking, and returns a job ID for tracking. This is adequate for an agent to understand the tool's behavior, though more details on error handling or job lifecycle could improve it.

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?

Three sentences, front-loaded with the core action and key behavior. Every sentence adds value: purpose, async nature, return value. No wasted words.

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?

For a simple queue tool, the description covers purpose, behavioral traits, parameters, and return value. No output schema exists, but the description specifies the job ID return. It is complete for an agent to select and invoke correctly.

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?

With 100% schema coverage, the description adds no extra meaning beyond the schema. It briefly mentions the return value (job ID) but not parameter-specific details. The schema already documents all parameters well, so 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 tool queues a document for asynchronous entity extraction, distinguishing it from synchronous extraction tools like extract_entities. It specifies the verb 'queue' and the resource 'document for entity extraction', making the purpose unambiguous.

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?

The description advises using this tool for non-blocking extraction, implying it is suitable when the agent should not wait for processing. It implicitly contrasts with synchronous extraction, but lacks explicit when-not-to-use guidance or direct mention of sibling alternatives.

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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