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detect_emotion

Analyze user text to detect emotions (28-class taxonomy) and compute adaptive sampling parameter deltas for personality-driven AI responses using Big Five scores.

Instructions

Classify the emotional tone of user text and compute adaptive sampling parameter adjustments.

Uses SamLowe/roberta-base-go_emotions (28-class taxonomy, ~100MB RAM, runs on Apple Silicon MPS). Returns the top-5 detected emotions and AutoTune parameter deltas that should be ADDED to the soul's base personality parameters before calling Ollama.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe user's message text to analyze.
soul_opennessNoSoul's Big Five Openness score (0-100).
soul_conscientiousnessNoSoul's Big Five Conscientiousness score (0-100).
soul_extraversionNoSoul's Big Five Extraversion score (0-100).
soul_agreeablenessNoSoul's Big Five Agreeableness score (0-100).
soul_neuroticismNoSoul's Big Five Neuroticism score (0-100).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description provides memory usage, runtime environment, and output details (top-5 emotions and deltas). It doesn't cover error handling or rate limits, but is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short paragraphs, front-loaded with the main purpose. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and available schema/output, the description covers what the tool does, how parameters are used, and what it returns. Leaves little ambiguity for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds context about the 'Big Five' scores and their role in AutoTune, but most parameter meaning is already in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool classifies emotional tone and computes adaptive parameter adjustments. It specifies the exact model and taxonomy, and differentiates from siblings (none similar).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explains the deltas are to be added before calling Ollama, giving clear context. It doesn't explicitly exclude alternatives, but no similar tools exist in siblings.

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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