Extract named entities from text — people, organizations, locations, dates and miscellaneous — returned as structured JSON.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to extract named entities from. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the full burden. It states the output is structured JSON but does not disclose performance characteristics, language support, or confidence scores. For a simple extraction tool, this is minimally adequate but lacks depth.
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 that front-loads the core purpose. Every word is meaningful, and there is no extraneous information.
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?
With no output schema, the description should specify the output structure. It mentions 'structured JSON' but does not detail the format. Edge cases (empty text, language) are not addressed. Overall, minimally complete for a simple tool.
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?
The only parameter 'text' is described in the input schema ('The text to extract named entities from.'). The description adds that the result is returned as structured JSON, but this does not significantly augment the schema's meaning. Schema coverage is 100%, so baseline 3 applies.
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 extracts named entities from text, listing the entity types (people, organizations, locations, dates, miscellaneous) and the output format (structured JSON). This distinguishes it from sibling tools like chat, classify, etc.
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?
No guidance on when to use this tool versus alternatives, such as when to prefer classify or summarize. The description does not mention 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.