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davidmosiah

Wellness Nourish

by davidmosiah
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Hydration summary

nourish_hydration_summary
Read-onlyIdempotent

Summarize daily hydration for any date to track water intake and spot trends. Choose summary, structured, or raw output for the desired detail level.

Instructions

Summarize local hydration for a date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNo
privacy_modeNosummary = high-level totals without item detail; structured = default full JSON-friendly fields; raw = same as structured for this local store (parity).structured
response_formatNojson

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.7.2
    • addedInput schema / properties / privacy_mode
      Added value: +{
+  "default": "structured",
+  "description": "summary = high-level totals without item detail; structured = default full JSON-friendly fields; raw = same as structured for this local store (parity).",
+  "enum": [
+    "summary",
+    "structured",
+    "raw"
+  ],
+  "type": "string"
+}
  2. First observedv0.6.6

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, correctly reflecting the summary operation. The description adds no additional behavioral context (e.g., data freshness, permission requirements), so while it does not contradict, it also does not enrich beyond the structured fields.

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 a single concise sentence with no filler. It is efficient, though it could be slightly expanded to include critical parameter notes without losing brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and moderate complexity (3 parameters, 2 enums), the description provides the core purpose but omits details about the summary content (e.g., totals vs. itemized) and behavior of different privacy_mode values. It is adequate but not fully complete for an agent to predict output without trial.

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

Parameters2/5

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

Schema description coverage is only 33% (only privacy_mode has a description). The tool description does not explain any parameters, including the date format or the effect of response_format. It fails to add meaning beyond the schema, especially for the undocumented parameters.

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?

The description clearly states 'Summarize local hydration for a date', using a specific verb, resource, and scope. It distinguishes itself from sibling summary tools like nourish_carbon_summary and nourish_daily_summary by explicitly focusing on hydration.

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?

No guidance is given on when to use this tool versus alternatives such as nourish_list_intake or nourish_clear_hydration_day. There is no mention of prerequisites or preferred scenarios, leaving the agent to infer usage from the name alone.

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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