MyFitnessPal MCP Server

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mfp_set_water

Set water intake for a day by specifying cups consumed. Accepts a date, defaulting to today, to log hydration in MyFitnessPal.

Instructions

Log water intake for a specific date.

Sets the number of cups of water consumed for the day. MyFitnessPal uses
cups as the unit (1 cup = ~237ml).

Args:
    params: SetWaterInput containing:
        - cups (float): Number of cups of water (e.g., 2.5 for 2.5 cups)
        - date (str, optional): Date in YYYY-MM-DD format, defaults to today

Returns:
    str: Confirmation message with the logged water amount

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_water"
  }
}

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

A3.8/5.0

Behavior4/5

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

Beyond the annotations, the description reveals that this tool 'sets the number of cups' for the day, implying an assignment/overwrite behavior rather than an additive log. It also adds the useful unit conversion (1 cup ≈ 237 ml), which helps the agent interpret values correctly.

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?

The description is front-loaded with the core purpose, followed by compact args and returns sections. It is reasonably sized with no wasted prose, though the args list partly duplicates the schema.

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 mutation tool, the description covers what it does, the parameters, defaults, and return value. The schema additionally provides bounds and enum-free types, so an agent has enough context to call it correctly. A minor gap is that it does not explicitly discuss replacing prior water entries for the same date.

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?

The description enumerates both parameters, their expected types, the date format, and the default behavior. Even with the schema documenting similar details, the description adds the unit conversion context and restates the schema semantics in a directly actionable form.

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 states the specific action ('Log water intake') and resource ('for a specific date'), plus the unit of measurement. It is unambiguous relative to read-only siblings like mfp_get_water, but it does not explicitly name or contrast any alternative tool.

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

Usage Guidelines3/5

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

The description implicitly tells the agent when to use it: when the goal is to log or set daily water intake. However, it provides no explicit guidance about alternatives or when not to use it, leaving the decision to inference.

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