Classify a text into exactly one of the candidate labels you provide (e.g. sentiment, topic, intent).
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to classify. | |
| labels | Yes | Candidate labels to choose from, e.g. ['positive','negative','neutral']. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits. It only says it classifies into one label, but omits details like output format, confidence, error behavior, or any side effects. This is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence placed at the start. Every word is functional, though it could benefit from additional behavioral details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 2-parameter tool with no output schema or annotations, the description is adequate but incomplete. It lacks details on output format or usage constraints, leaving some ambiguity for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. The description adds value by specifying that the output is exactly one label and providing examples for the labels array, clarifying the classification behavior beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool classifies text into exactly one of provided labels, with examples like sentiment, topic, intent. This is a specific verb-resource description that distinguishes from sibling tools like chat or summarize.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for classification tasks via examples, but does not explicitly state when to use this tool versus siblings, nor does it provide exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool addresses a distinct task (chat, classify, embed, extract entities, summarize, transcribe) with no functional overlap. An agent can easily distinguish which tool to use for a given request.
All tool names use an imperative verb form (chat, classify, embed, extract_entities, summarize, transcribe). The pattern is consistent and intuitive, with only 'extract_entities' using an underscore but still following the verb_noun pattern.
Six tools is an ideal scope for a general AI toolkit, covering a broad range of common NLP tasks without being overwhelming. Each tool serves a clear purpose, and the count feels well-proportioned.
The tools cover essential AI capabilities: text generation, classification, embeddings, NER, summarization, and audio transcription. Minor gaps like translation or image analysis exist, but the set is comprehensive for core NLP tasks.