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set_client_macro_targets

Set a client's daily targets: calories and/or grams of protein, carbs and fat. No meal plan needed. Replaces the previous targets (send all numbers you want kept). Optional preset BALANCED / HIGH_PROTEIN / LOW_CARB fills any gram value you leave out from the calories. The client app shows these as the goals. (confirm-gated: previews until confirm: true)

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": {
      "clientId": "<clientId>",
      "input": "<input>"
    },
    "name": "set_client_macro_targets"
  }
}

Input Schema

NameRequiredDescriptionDefault
inputYes
confirmNoMust be true to actually execute. If false/omitted, returns a preview only.
clientIdYesClient's User _id (from list_clients)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations only declare readOnlyHint=false, so the description carries the burden and does it well: it discloses the destructive replace semantics, the confirm-gated preview-until-true workflow, how the preset backfills missing grams, and that the values surface as goals in the client app.

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?

Front-loads the action and fields, then layers replacement semantics, preset behavior, user-visible effect, and the confirm gate in short sentences with no filler.

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?

For a nested-input mutation with no output schema, it covers the essentials an agent needs: what is overwritten, how to preserve values, the preset interaction, and the preview gate. Only the explicit relationship to sibling tools (clearing/reading targets) is left implicit.

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 coverage is 67% and already documents clientId, confirm, and preset, but the description adds real meaning: the preset consumes calories and fills omitted gram fields, and all retained numbers must be resent because of the replace behavior. It does not discuss the numeric bounds, though those are 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?

States a specific verb and resource ('Set a client's daily targets') plus the exact fields covered (calories, protein, carbs, fat), which cleanly separates it from get_client_macro_targets, clear_client_macro_targets, and calc_macros in the sibling list.

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?

Gives clear context for use: no meal plan is required, and the tool replaces rather than patches, so the agent must resend every value it wants kept. It does not name sibling alternatives (e.g., clear_client_macro_targets for removal, calc_macros for deriving values), so it stops short of explicit alternative routing.

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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