aivy-mcp

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update_ai_plus

Set AI+ settings and switch engine variants for a solar site; values are validated first, so rejected inputs leave the current configuration unchanged.

Instructions

CHANGES THE SITE (control scope). Confirm with the user before calling.

Set Ai+ levers (names and ranges from get_ai_plus tunable) and/or switch engine variant. Validated against the variant that will run; if any value is rejected nothing changes and rejected says why. Does not switch the mode: use set_energy_mode(mode='amber_smart') for that.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "site_id": "<site_id>"
    },
    "name": "update_ai_plus"
  }
}

Input Schema

Name	Required	Description	Default
`site_id`	Yes
`variant`	No
`settings`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.9.0Nov 6, 2026

TDQS

A4.6/5.0

Behavior4/5

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

No annotations exist, so the description carries the full disclosure burden and does well: it reveals atomic all-or-nothing validation against the resolved variant and that `rejected` explains failures. It omits permission/authentication requirements and any indication of side effects beyond the Ai+ settings themselves.

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?

Front-loads the mutation warning in capitals, then purpose, then failure semantics, then the sibling exclusion — a sensible priority order with essentially no filler. The telegraphic phrasing of the third sentence ('Validated against the variant that will run') is slightly compressed but still parseable.

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?

For a three-parameter mutation tool with an output schema (so return values need no explanation), the description covers the safety warning, valid-input sourcing, failure behavior, and the adjacent mode tool. Nothing an agent needs in order to call it correctly is missing.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate, and it does for the two non-obvious parameters: `settings` is defined as Ai+ levers sourced from get_ai_plus `tunable`, and `variant` as the engine variant to run. `site_id` is self-evident and left unexplained, and the free-form settings object's expected key shape is only indirectly described.

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?

States a specific verb and resource — setting Ai+ levers and/or switching the engine variant — and immediately bounds the scope with 'CHANGES THE SITE (control scope)'. It also explicitly separates itself from set_energy_mode, so an agent can distinguish it from sibling tools without opening any schema.

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

Usage Guidelines5/5

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

Gives a precondition ('Confirm with the user before calling'), tells the agent where to get valid lever names and ranges (get_ai_plus `tunable`), and names the alternative for mode changes (set_energy_mode(mode='amber_smart')). Both when-to-use and a clear when-not-to-use are present.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.