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demandscope

demandscope

by baobabcat
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

pypi_downloads

Retrieve last-month download counts for any PyPI package to gauge comparable-product demand and validate digital-product ideas.

Instructions

Last-month downloads for a PyPI 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": "pypi_downloads"
  }
}

Input Schema

NameRequiredDescriptionDefault
packageYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A3.5/5.0
Behavior2/5

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

There are no annotations, so the description alone must disclose behavior. It only reveals the time window ('last-month') and the resource, but gives no details about return format, error handling, edge cases like unknown packages, or whether any side effects exist. The description carries minimal behavioral burden for a tool with no annotation support.

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 that immediately leads with the primary output metric and resource. There is no filler, and the parenthetical adds useful context without bloating the definition.

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 simple read-only tool with one parameter and no output schema, the description covers the essential input semantics and the metric returned. However, it omits details like what the response looks like, how 'downloads' are counted, and behavior for invalid or unknown packages. Given the absence of annotations, this is adequate but not fully complete.

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?

The input schema has one parameter ('package') with zero description coverage. The description adds meaningful semantics by identifying it as a PyPI package, which tells the agent what value to provide. It doesn't specify format or examples, but for a single-parameter tool this is a significant clarification over the raw schema.

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

Purpose5/5

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

The description clearly states that the tool returns last-month downloads for a PyPI package, which is a specific resource and time window. The phrase 'PyPI package' also distinguishes it from siblings like npm_downloads, GitHub, or Hacker News tools, even without explicitly naming them.

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?

No guidance is given on when to use this tool versus alternatives. The parenthetical 'comparable-product demand' hints at a possible use case, but it does not explain when to choose pypi_downloads over npm_downloads or other sibling tools, nor does it mention any exclusions or prerequisites.

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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