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demandscope

demandscope

by baobabcat
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

npm_downloads

Retrieve last-month download counts for any npm package to gauge real-world demand and validate product-market fit.

Instructions

Last-month downloads for an npm package (comparable-product demand).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "package": "<package>"
    },
    "name": "npm_downloads"
  }
}

Input Schema

NameRequiredDescriptionDefault
packageYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral disclosure burden. It clearly indicates this is a download-count lookup, but it does not define whether 'last-month' means the previous calendar month or a trailing 30-day window, nor does it describe the return format, data source, or error behavior.

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

Conciseness5/5

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

The description is a single sentence with the core output stated first and the use-case context added parenthetically. It is concise and every word contributes meaning.

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

Completeness3/5

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

For a one-parameter tool, the description gives enough to understand the basic input and output, but it omits precise time-window semantics, output shape, and failure cases. Since there is no output schema and no annotations, the description is minimally adequate rather than complete.

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 0%, so the description must clarify the 'package' parameter. It adds that the parameter refers to an npm package, which is useful, but it does not provide examples or mention format details such as scoped package names.

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?

The description states a specific metric ('Last-month downloads') for a specific resource ('npm package'), which clearly conveys what the tool returns. It is distinguishable from pypi_downloads by the npm scope, though it does not explicitly contrast with siblings.

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 parenthetical '(comparable-product demand)' implies a use case, but there is no explicit guidance on when to choose this tool over pypi_downloads or the GitHub/HN alternatives. No exclusions or alternative routing are provided.

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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