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sanhao-global-experts

money-saving-pro

Turn expertise into output. This money saving pro 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?

No annotations exist, so the description carries the full burden, and it discloses little: it says the pack 'includes' a prompt, usage guide, and edge-case handling, which hints at the response payload, but never states whether the tool is read-only, what the agent should do with the returned prompt, or any constraints. The 'edge-case handling' claim is unexplained.

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?

Three sentences, the first of which ('Turn expertise into output') is pure marketing filler that conveys nothing about the tool. The remainder is packaged as promotional copy rather than front-loaded functional detail.

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?

With no annotations and no output schema, the description must explain the interaction model, and it does not: an agent cannot tell whether invoking this returns a reusable prompt string, a savings analysis, or a guide file. For a single-parameter pack tool this is under-specified.

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?

There is one optional 'query' parameter with full schema description coverage, so the schema documents it adequately. The description adds no meaning about what the query scopes or how it changes output, so the baseline of 3 applies.

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 leans on marketing framing ('Turn expertise into output', 'produce professional results in minutes') rather than stating a concrete verb and resource. It never says what the tool actually returns (a prompt template? a savings plan?) or distinguishes it from the sibling money-saving-plan. The only concrete facts are the audience (restaurant and brand teams) and the bundle contents.

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?

There is no when-to-use or when-not-to-use guidance, and no mention of alternatives such as money-saving-plan or the fnb-* cost tools that overlap with the restaurant audience. Audience targeting ('restaurant and brand teams') is the only weak usage signal.

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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