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by app.vitalli.mcp
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joiva_log_water

Log plain water, sparkling water or salt water the signed-in user drank, in millilitres. Water lives in its own ledger: no macros, never breaks a fast. Drinks with content (coffee, tea, milk, cocoa, juice, spritzer, cola, beer, wine, broth) are not water: they belong in the food log with caffeine, alcohol and energy, and their water still counts towards the hydration target. For backwards compatibility kind: 'coffee' is logged as a plain coffee in the food log. The daily hydration target and its breakdown are part of the day view under water. kind: 'salt_water' records the salted water of an extended fast with its sodium, which the fasting safety rule depends on. Time defaults to now.

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": {
      "ml": "<ml>"
    },
    "name": "joiva_log_water"
  }
}

Input Schema

NameRequiredDescriptionDefault
mlYesAmount in millilitres, e.g. 250 for a glass
tzNoIANA timezone, default Europe/Berlin
kindNoDrink type, default 'water'; 'salt_water' = salted water during a fast; 'sparkling_water' = sparkling water, counts like water (sodium unknown).
loggedAtNoISO 8601 timestamp with offset, defaults to now
sodiumMgNoSodium in mg, for salt water
plannedDateNoYYYY-MM-DD: a drink planned for a future day

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / kind / description
      Previous value: -"Drink type, default 'water'; 'salt_water' = salted water during a fast."New value: +"Drink type, default 'water'; 'salt_water' = salted water during a fast; 'sparkling_water' = sparkling water, counts like water (sodium unknown)."
    • changedInput schema / properties / kind / enum
      Previous value: -[
-  "water",
-  "salt_water",
-  "coffee",
-  "tea",
-  "other"
-]New value: +[
+  "water",
+  "salt_water",
+  "sparkling_water",
+  "coffee",
+  "tea",
+  "other"
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations only declare the generic write profile (not read-only, not destructive, not idempotent); the description adds real behavioral context: water has no macros and never breaks a fast, salt_water's sodium feeds the fasting safety rule, and legacy 'coffee' is rerouted to the food log. It does not mention deduplication or repeat-call behavior, which matters given idempotentHint=false.

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-loaded with the core action, then scope, then edge cases; every sentence carries information. The dense colon-separated drink list and the coffee-compatibility aside make it heavier than necessary but not padded.

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 six-parameter write tool with no output schema, the description covers domain boundaries, side effects on fasting/hydration, and the legacy edge case. It omits what the call returns and any indication of how the logged entry affects the day summary, leaving a small gap.

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 100%, so the baseline is 3, but the description adds semantics the schema cannot express: the backwards-compatibility behavior of `kind: 'coffee'`, and that salt_water's `sodiumMg` is consumed by the fasting safety rule. `plannedDate`, `tz`, and `loggedAt` are left entirely to 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 ('Log plain water, sparkling water or salt water the signed-in user drank, in millilitres') and immediately scopes it to a separate water ledger. An agent can distinguish it from joiva_log_food / joiva_log_drink 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 Guidelines4/5

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

Explicitly enumerates what counts as water versus what belongs in the food log (coffee, tea, milk, juice, beer, wine, broth) and notes their water still counts toward hydration. It stops short of naming the sibling tool to use instead, so the routing is implied rather than explicit.

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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