L4 Somatic Emotion Decode: converts body-sensation text (e.g. tight chest, clenched fists) into structured emotion: primary affect, valence/arousal, emotion texture, causal intent, plus somatic anchor cues. Grounded in the somatic decoding discipline; wellness simulation only, not a diagnostic tool.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It does disclose that the tool is 'wellness simulation only, not a diagnostic tool' and mentions its grounding in the somatic decoding discipline. It remains silent on potential side effects, data handling, or failure behavior, but as a pure text-to-structure converter these are less critical.
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, well-structured sentence that front-loads the action, gives examples, lists output components, and adds scope limitations. Every clause contributes useful information without redundancy or filler.
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 simplicity of the single-parameter schema and the presence of an output schema documenting return values, the description covers the essential context: input type, examples, output structure, and usage boundaries. Only minor additions like explicit alternative guidance would make it more complete.
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 schema has 0% description coverage and only names the parameter 'text'. The description compensates well by defining the input as 'body-sensation text' and providing concrete examples ('tight chest, clenched fists'), which gives the agent a clear understanding of expected parameter content. It could add length or format constraints, but the core semantics are clear.
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 uses a specific verb ('converts') with a clear resource ('body-sensation text') and enumerates the structured output components (primary affect, valence/arousal, emotion texture, causal intent, somatic anchor cues). This differentiates it from generic siblings like analyze_text and empathy_hint.
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?
It clearly indicates when to use the tool (for body-sensation text) and provides an explicit exclusion ('not a diagnostic tool'). However, it does not name or contrast with any sibling tools such as analyze_text or empathy_hint, leaving some routing decisions to inference.
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.