L1 Affect Extraction: analyze text into an 8-dimensional emotion vector, emotion texture labels, causal intent classification, and a natural-language state description. text: input up to 2000 chars. lang: optional 'zh' or 'en'.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | ||
| text | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the disclosure burden. It clearly explains what the tool produces and constrains input size and language options, which covers the main behavioral surface for an analysis tool. It does not mention side effects such as data retention or authentication, but those are less critical for this kind of text-analysis operation.
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 two dense sentences with no filler. The tool's action and output types are front-loaded, followed by concise parameter guidance, making it easy to parse quickly.
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 tool with an output schema and an unrelated sibling, this description is nearly complete: it covers purpose, outputs, input limits, and supported language values. It could be slightly stronger by mentioning usage exclusions or any special response interpretation, but nothing essential for invoking the tool is missing.
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 description coverage is 0%, but the description compensates fully. It explains the text parameter as input up to 2000 characters and lang as an optional parameter accepting 'zh' or 'en', which adds meaningful semantics that the raw schema lacks.
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 begins with 'L1 Affect Extraction: analyze text into...' and enumerates four concrete output types: an 8-dimensional emotion vector, emotion texture labels, causal intent classification, and a natural-language state description. This makes the tool's purpose unambiguous and distinctly separate from the unrelated sibling tool generate_avatar_params.
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 gives clear invocation context by specifying that text is input up to 2000 characters and lang is optional with 'zh' or 'en' values. It does not explicitly state when not to use the tool or point to alternatives, but the only sibling tool is unrelated, so there is no real routing ambiguity.
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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Each tool is clearly pinned to a distinct stage and input: raw text emotion analysis, avatar parameter driving, empathy response strategy, and somatic body-sensation decoding. Even though analyze_text and somatic_decode share emotion-output dimensions, the text vs. body-sensation input boundary plus the explicit L1/L4 labels make misselection unlikely.
The names use snake_case but mix conventions: analyze_text and generate_avatar_params are verb_noun, while empathy_hint is noun_noun and somatic_decode reads as adjective+verb. This is readable but less predictable than a uniform verb_noun pattern.
Four tools map cleanly onto the four advertised affect-processing levels (L1-L4) with no redundant duplicates. The count feels deliberately scoped for a focused affective-computing API.
The surface covers the core lifecycle from text/body input through affect analysis, avatar parameters, and empathy strategies, so the main workflows are present. Minor gaps exist around cross-chaining outputs (e.g., a somatic decode cannot directly feed the avatar or empathy tools) and there is no batch/status endpoint, but these are workable.