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Living Body — Ask

living_body_ask
Read-only

Ask wellness questions about training, recovery, sleep, nutrition, or readiness. Composes data from your connected health devices and returns a synthesized answer with reasoning.

Instructions

Ask a wellness question; composes detected connectors in parallel and returns a synthesized answer + reasoning trace. Spawns subprocesses — requires explicit_user_intent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourcesNoRestrict composition to a subset of detected connectors. Defaults to all detected.
questionYesNatural language question about training, recovery, sleep, nutrition or readiness.
privacy_modeNoPassed down to child connectors. Defaults to 'structured'. 'raw' is only honored with explicit_user_intent and is discouraged for routine composition.
response_formatNomarkdown
explicit_user_intentYesRequired confirmation that the caller is acting on explicit user intent. living_body_ask spawns multiple wellness MCP subprocesses; agents must not call it speculatively.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasoningYes
confidenceYes
generated_atYes
sources_usedYes
data_snapshotYes
recommendationYes
sources_failedYes
Behavior5/5

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

Beyond annotations (readOnlyHint, openWorldHint), the description discloses key behaviors: parallel connector composition, subprocess spawning, and return of reasoning trace. It adds context about privacy_mode passing and the requirement for explicit user intent, aligning with openWorldHint without contradiction.

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?

The description is three sentences, front-loaded with the main action, and contains no redundant information. Every sentence adds essential context.

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 existence of an output schema, the description adequately covers return values (synthesized answer + reasoning trace) and core behaviors. Minor gaps exist (e.g., no mention of rate limits or error handling), but overall it is sufficient for a tool with clear annotations and schema.

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 description coverage is high (80%), so the description adds limited per-parameter value. It does not elaborate on individual parameters beyond the schema's explanations, making the contribution marginal. Baseline 3 is appropriate.

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's function ('Ask a wellness question'), specifies it composes connectors in parallel, and returns a synthesized answer with reasoning trace. It distinguishes from sibling tools like living_body_capabilities or daily_brief by emphasizing question-answering and subprocess spawning.

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?

The description explicitly requires 'explicit_user_intent' and warns agents not to call speculatively due to subprocess spawning. It provides clear context for when to use (with explicit user intent) but does not mention when not to use or suggest alternatives among 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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