MyFitnessPal MCP Server

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mfp_set_goals

Idempotent

Update daily nutrition goals for calories, protein, carbs, and fat. Set new targets for specific nutrients while leaving others unchanged.

Instructions

Update daily nutrition goals (calories, protein, carbs, fat).

Sets new daily targets for the specified nutrients. Only updates the
values that are provided; others remain unchanged.

Args:
    params: SetGoalsInput containing:
        - calories (int, optional): Daily calorie goal
        - protein (int, optional): Daily protein goal in grams
        - carbohydrates (int, optional): Daily carb goal in grams
        - fat (int, optional): Daily fat goal in grams

Returns:
    str: Confirmation message with updated goals

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": {
      "params": "<params>"
    },
    "name": "mfp_set_goals"
  }
}

Input Schema

Name	Required	Description	Default
`params`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 13, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already cover idempotency, non-read-only, and non-destructive behavior. The description adds valuable behavioral context beyond those annotations by explicitly stating that only provided values are updated and others remain unchanged, plus noting the confirmation string return.

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 well-structured with a clear opening statement, partial-update caveat, labeled Args section, and Returns section. Every sentence adds useful information without redundancy or bloat.

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 simple update tool with four optional fields, the description provides sufficient information: what is updated, the partial-update behavior, parameter meanings, and return type. The main missing piece is guidance about which sibling should be used instead, but the core calling context is adequately covered.

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 reported as 0%, so the description must compensate. It lists all four parameters with their types, optionality, and units (grams where applicable). This meaningfully helps an agent understand what each parameter controls, even though the nested schema also contains similar descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the verb ('Update') and resource ('daily nutrition goals' for calories, protein, carbs, fat). It is clear and specific, but it does not explicitly distinguish itself from similar sibling tools such as mfp_set_nutrient_goals or mfp_get_goals.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives like mfp_get_goals or mfp_set_nutrient_goals. The partial-update behavior is described, but no explicit when/when-not conditions or alternatives are mentioned.

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