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DropshippingLeads MCP

Read public dataset coverage and release

get_data_coverage
Read-onlyIdempotent

Read public dataset status, published release information, English category names and counts. Use the returned category names for searching; their original Chinese aliases are also accepted. It does not describe the private commercial database or guarantee all categories are represented.

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

The annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description's added value comes from the data-completeness caveat: no private database and no guarantee that all categories are represented. That caveat, which shapes how far an agent should trust the result, is exactly the kind of context the annotations cannot supply.

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

Conciseness4/5

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

Three short sentences that lead with what is returned and then add caveats. They contain no filler, though the sentence about downstream use of category names is worded awkwardly and could be stated more tightly.

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?

There is no output schema, so the description must carry the burden of the return values; it enumerates the returned content (status, release info, category names and counts), which is adequate for a metadata-read tool. The exact response shape is still left implicit, but nothing critical to call it correctly is missing.

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?

This tool takes zero parameters, so the baseline score of 4 applies. The description adds no parameter detail, but there is nothing to detail.

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?

States a specific verb and resource combination ('Read public dataset status, published release information, English category names and counts'), which is far more informative than a tautology. The scope is clearly distinct from the merchant and methodology siblings, though it never names them to make the contrast explicit.

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 description gives a downstream usage hint ('Use the returned category names for searching') and one exclusion ('does not describe the private commercial database'). However, it never says when an agent should call this tool versus get_methodology or search_merchants, so the trigger condition is only implied.

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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