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Usual response to a meal

get_meal_response
Read-onlyIdempotent

What happened in the user's own data after similar meals they logged before: median rise, usual range, minutes to peak, n and confidence. It is the past, not a prediction. Always a full query.

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": {
      "meal": "<meal>"
    },
    "name": "get_meal_response"
  }
}

Input Schema

NameRequiredDescriptionDefault
mealYesThe meal, in the user's words.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive, and closed-world behavior. The description adds valuable context beyond that: the specific returned statistics, the historical-not-predictive nature, and the "always a full query" execution trait. It does not cover latency, errors, or permission requirements.

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?

Three tight sentences, front-loaded with what is returned. Every sentence adds distinct information: output metrics, historical scope, and query behavior.

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, one-parameter, read-only query with no output schema, the description is largely complete: it names the return metrics and clarifies that results are historical. It leaves minor gaps around matching criteria and units, but an agent can call it correctly.

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 schema already defines the sole `meal` parameter. The description adds meaning by indicating that the meal string is used to match similar previously logged meals, not just an exact label, which is useful beyond the schema's "user's words" phrasing.

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 states a specific retrieval purpose: historical response metrics (median rise, usual range, minutes to peak, n, confidence) after similar logged meals. It clearly distinguishes itself from logging siblings by emphasizing past data, though it does not name a sibling explicitly.

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 phrase "It is the past, not a prediction" implies when this tool is appropriate versus a forecasting tool, but no alternatives are named and no explicit when-not guidance is given. "Always a full query" hints at usage behavior without fully clarifying it.

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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