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sanhao-global-experts

credit-score-coach

Turn expertise into output. This credit score coach 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

C2.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It says the pack includes a prompt, usage guide, and edge-case handling, but does not disclose whether invocation generates text, retrieves packaged content, requires authentication, has side effects, or what the response format is.

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

Conciseness3/5

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

The description is short and includes one useful sentence about contents, but it is not front-loaded with a concrete operation. The opening "Turn expertise into output" and the marketing-style "produce professional results in minutes" add little operational information.

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 should clarify the return value and invocation behavior for this prompt-pack tool. It mentions included materials but does not explain what calling the tool produces, leaving the agent without enough context to invoke 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 in the input schema. The description adds no meaning about how the query scopes the analysis or what happens when it is omitted, but the high schema coverage supports the baseline score of 3.

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

Purpose3/5

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

The description identifies a "credit score coach prompt pack" and says it includes a prompt, usage guide, and edge-case handling, but it never states the concrete action the tool performs or what an agent gets back. "Turn expertise into output" and "produce professional results" are generic, and the target audience of restaurant and brand teams is unexpected for a credit-score tool.

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?

It names an intended audience (restaurant and brand teams) but gives no when-to-use guidance, no exclusions, and no alternatives among the many sibling tools. It does not explain when this prompt pack should be selected over credit-card-optimizer, debt-snowball-plan, or other finance-related tools.

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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