L2 Avatar Driving Pipeline: one call returns blendshape/AU/curve animation parameters, lighting & material atmosphere package, adapter payload, plus the upstream L1 affect analysis. Requires a key tier that includes L2.
| 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?
There are no annotations, so the description must carry the burden of behavioral disclosure. It does disclose that one call aggregates multiple output packages and that an L2 key tier is required. However, it does not mention failure behavior, side effects, latency, or what happens when the key tier 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 dense sentence with no wasted words, and the key pipeline identifier is front-loaded. It earns its length but uses heavy jargon (blendshape/AU/curve, adapter payload, atmosphere package) that could reduce quick comprehension.
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 an output schema present, describing return values is less necessary, and the description does add useful context about the pipeline level and key tier requirement. However, it omits parameter guidance and any explicit relationship to the sibling tool, leaving an agent to infer important selection and input details.
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%, and the description never explains what `text` should contain, its format, length, or role in the pipeline. The only clue is the tool name and the schema's `text` property, so the description does not compensate for the missing parameter documentation.
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 identifies a specific pipeline ('L2 Avatar Driving Pipeline') and states that a single call returns blendshape/AU/curve parameters, lighting and material packages, adapter payload, and L1 affect analysis. This clearly differentiates it from the sibling analyze_text by positioning it as the L2 generation tool rather than L1 text analysis.
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 when to use the tool: when L2 avatar driving parameters are needed and the account has a key tier including L2. However, it does not explicitly contrast it with analyze_text or state when NOT to use it, leaving the selection logic somewhat implicit.
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 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.