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get_human_state

Retrieve a user's real-time stress level and unified state to adapt AI responses appropriately, ensuring optimal engagement based on current human state.

Instructions

Get current unified human state for a session. Call this before generating important responses.

Returns:
- state: calm | relaxed | focused | stressed | acute_stress
- stress_score: 0-100 (lower = calmer)
- confidence: 0.0-1.0 (based on signal quality and device type)
- suggested_action: maintain_engagement | simplify_and_focus | de-escalate_and_shorten | pause_and_ground
- action_reason: human-readable explanation of why this action was suggested
- adaptation_effectiveness (on 2nd+ call): shows whether your previous suggested_action actually reduced stress — contains previous_action, stress_delta, and effective boolean. Use this to self-improve.

Use suggested_action to adapt your response: calm/relaxed = full complexity, focused = shorter and structured, stressed = max 2 sentences, acute_stress = one grounding sentence only.

Requires a prior ingest call to have data. Not a medical device.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idYes
Behavior5/5

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

With no annotations, the description fully carries the burden. It discloses the tool is not a medical device, requires prior ingest, and explains the adaptation_effectiveness field on 2nd+ calls. Returns fields are described in detail, including scale ranges (stress_score 0-100, confidence 0.0-1.0) and meanings of suggested_action values. No contradiction.

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

Conciseness4/5

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

Description is informative and front-loaded with purpose. It lists return fields with explanations. A few extra sentences (e.g., adaptation_effectiveness details) add value but increase length. Could be slightly trimmed, but overall structured well.

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

Completeness5/5

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

Given the tool's simplicity (1 param, no output schema, no annotations), the description is comprehensive: covers return values, usage guidance, adaptation feedback, prerequisite, and disclaimer. Leaves no major gaps for an agent to understand when and how to use it.

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?

Only one parameter (session_id) with 0% schema description coverage. The description does not explicitly describe session_id, but the context (session state) makes it clear. Since schema offers no help, description should ideally add format or source, but the parameter name and tool purpose are self-explanatory. Adequate but not enriched.

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?

Description clearly states it gets the unified human state for a session, specifying when to call it ('before generating important responses'). It lists return fields (state, stress_score, etc.) and differentiates from sibling tools like get_session_history or get_trigger_memory by focusing on human state.

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?

Explicitly says 'Call this before generating important responses.' Provides guidance on how to use suggested_action to adapt response complexity based on state. Mentions requirement for prior ingest call. Could be clearer on when not to use (e.g., for trivial responses), but context is sufficient.

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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