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sanhao-global-experts

mortgage-prepay-calc

Turn expertise into output. This mortgage prepay calc 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

C2/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full behavioral burden. It discloses that the tool includes a prompt, usage guide, and edge-case handling, but it does not explain whether it computes results, emits a template, requires domain inputs, or what the agent should expect as output.

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 opening sentence is generic filler and does not front-load any useful information. The second sentence mixes a mortgage purpose with an unrelated restaurant/brand audience, making the definition less clear despite being short.

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 is the main source of behavioral context, yet it leaves the core operation undefined. An agent still does not know whether this is a calculator, a prompt library, or a content-generation tool.

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?

The single optional `query` parameter is fully described in the schema, so the baseline is 3. The description adds no meaning about how the query scopes the analysis or what format it should take.

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 says this is a "mortgage prepay calc prompt pack," but immediately claims it helps "restaurant and brand teams," which is confusing for a mortgage tool. It never states whether the tool calculates mortgage prepayment, returns a prompt template, or generates guidance, so an agent cannot confidently distinguish its purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool, when not to use it, or what alternatives exist. Generic phrases like "Turn expertise into output" do not tell an agent which user requests should route here.

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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