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sanhao-global-experts

fatloss-meal-planner

Turn expertise into output. This fatloss meal planner prompt pack helps restaurant and brand teams produce professional results in minutes. Includes the complete prompt, usage guide, and edge-case handling.

Input Schema

NameRequiredDescriptionDefault
queryNoOptional user query to scope the analysis.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.2/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden and mostly fails it. It never says whether the tool returns static prompt text, a generated plan, or a file, nor whether the call is read-only or has side effects. The one concrete disclosure is that the pack "includes the complete prompt, usage guide, and edge-case handling," which hints at output contents but is not enough for an unannotated tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The text is short but front-loaded with filler: "Turn expertise into output" conveys zero operational information, and "produce professional results in minutes" is promotional padding. Only the final clause about included materials has any substance, and it arrives last.

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

Completeness2/5

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

A single optional parameter with no output schema and no annotations means the description must establish what the tool returns and how to invoke it usefully, and it does neither. The phrase "prompt pack" is never explained in terms of call behavior. Given the crowded sibling namespace, this leaves the agent without the information needed to call it confidently.

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

Parameters3/5

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

Schema description coverage is 100%, so the single optional "query" parameter is already documented as "Optional user query to scope the analysis." The description adds nothing about what a scoped query would influence or what format it should take. Per the baseline rule for full schema coverage, a 3 applies with no compensating detail from the description.

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

Purpose2/5

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

The description restates the tool name ("fatloss meal planner") and adds only the marketing framing "prompt pack," without stating what a call actually produces. "Turn expertise into output" and "produce professional results in minutes" are slogan phrases, not a verb+resource statement. An agent cannot tell whether this returns a meal plan, a prompt template, or guidance text.

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?

The only scoping clue is the audience ("restaurant and brand teams"); there is no statement of when to call this versus obvious siblings such as meal-prep-planner, fitness-diet-coach, or dinner-order-planner. No prerequisites, exclusions, or conditions are given. Usage has to be inferred entirely from the tool name.

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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