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extract

Identifies keywords, topics, domain, intent, sentiment, and entities from text using heuristic extraction—no LLM needed.

Instructions

Automatic semantic extraction from text: keyword, topic, domain, intent, sentiment, entities. Heuristic (0 token) — no LLM extraction.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText to analyze (user message)
contextNoContext path (e.g. java/spring). Defaults to active context.
Behavior3/5

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

Discloses heuristic nature and zero token cost, but with no annotations, lacks details on safety (e.g., destructive potential) or side effects.

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?

Single concise sentence captures purpose and key differentiator without unnecessary words.

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?

Lists extracted items but does not describe output format or limitations, which is needed since no output schema exists.

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% and description adds no extra meaning beyond the schema's parameter descriptions.

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?

Clearly states it performs heuristic semantic extraction (keyword, topic, etc.) and distinguishes itself by being token-free, but does not explicitly differentiate from siblings like summary or auto.

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?

Implies usage for fast extraction via 'heuristic (0 token)' but provides no explicit when/when-not or alternative tool guidance.

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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