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ober37

AC Infinity MCP

check_vpd_drift

Compare current VPD to target range for a growth stage and detect drift with actionable alerts.

Instructions

Check if current VPD is within target range for a growth stage.

Args: device_id: The AC Infinity device code (from discover_devices) stage: Growth stage - one of: clones, seedling, veg, early_flower, mid_flower, late_flower

Returns: JSON example::

    {
      "device_id": "C58ZA",
      "current_vpd": 1.58,
      "target_range": [1.0, 1.5],
      "stage": "veg",
      "status": "HIGH",
      "deviation": 0.08,
      "alert": "VPD 1.58 exceeds target 1.00–1.50. Raise humidity or lower temperature."
    }

``status`` is one of ``"OK"``, ``"LOW"``, or ``"HIGH"``.
``deviation`` is 0 when OK; positive when HIGH (kPa above upper bound);
negative when LOW (kPa below lower bound).
``alert`` is ``null`` when status is ``"OK"``.
On failure returns ``{"error": "...", "detail": "..."}``.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
device_idYes
stageNoveg

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided; description fully details behavior: status values (OK, LOW, HIGH), deviation semantics, alert field (null when OK), and error format. It discloses return structure comprehensively.

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?

Well-structured with Args and Returns sections, front-loaded purpose. Includes a helpful JSON example. Slightly lengthy but every sentence adds value; no fluff.

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 output schema is implicitly provided via example and field explanations, description covers return values, deviations, alerts, and error handling. Sufficient for an AI agent to use the tool correctly.

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

Parameters5/5

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

Input schema has 0% description coverage, providing only titles and types. Description adds meaning: device_id sourced from discover_devices, stage lists allowed values (clones, seedling, etc.) and default. This significantly enhances 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?

Description clearly states the tool checks if current VPD is within target range for a growth stage, specifying the verb 'check', resource 'VPD', and context 'growth stage'. It distinguishes from sibling tools like 'set_vpd_automation' and 'detect_environment_trends'.

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?

Description explains parameters and return values, implying use when needing to verify VPD compliance. However, it does not explicitly exclude alternative tools or provide when-not guidance, so it scores 4.

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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