Ask a general-purpose LLM a question or give it an instruction, with an optional system prompt. No external API key required.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The user's message or question for the assistant. | |
| system | No | Optional system instruction to steer the assistant's behaviour/persona. | |
| max_tokens | No | Maximum tokens to generate (default 512). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It adds value beyond the schema by noting 'No external API key required'. It clearly indicates it uses a general-purpose LLM, which sets expectations. It could be improved by mentioning any rate limits, context handling, or output format, but current transparency is adequate.
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 sentence with no filler. It front-loads the core purpose ('Ask a general-purpose LLM') and includes critical context ('No external API key required'). Every word is informative and earns its place.
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?
Given the tool's simplicity (3 parameters, all described), no output schema and sibling tools that are more specialized, the description covers the essential purpose and a key behavioral context. It could be improved by clarifying that the response is the LLM's text output, but that is implied for a chat 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 input schema has 100% coverage with descriptions. The description reinforces the optional system prompt and adds that max_tokens defaults to 512 (not in schema). This adds meaning beyond the schema, especially for new users unfamiliar with the tool's defaults.
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 states 'Ask a general-purpose LLM a question or give it an instruction', clearly specifying the verb (ask/give) and the resource (general-purpose LLM). It distinguishes itself from sibling tools like classify, embed, and summarize, which are specialized tasks, by being the general-purpose chat option.
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 mentions 'No external API key required', which implies ease of access compared to tools that might need keys. However, it does not explicitly state when to use this tool versus siblings like classify or summarize, nor does it provide when-not-to-use guidance.
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.