Machinepulse

Get Pypi Trends

get_pypi_trends

Read-onlyIdempotent

Fetch daily download counts for a PyPI package — up to 100 days, with total, latest day and average over days that have data; days without collection are null with a note. Read-only aggregation of daily-collected observatory data; no side effects. Days inside the PyPI reporting delay are partial (shown, excluded from latest and average), never fake zeros. Use when the user asks how a PyPI package is growing; for side-by-side comparison use get_package_comparison.

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": "get_pypi_trends"
  }
}

Input Schema

Name	Required	Description	Default
`days`	No
`package`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 21, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, but the description goes well beyond them: missing days are null with a note, PyPI reporting-delay days are partial and excluded from latest/average, and there are never fake zeros. That data-quality behavior is exactly what an agent needs to interpret results correctly.

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?

Front-loaded with the core action and scoped with the alternative at the end; every clause carries information. It is dense with em-dash parentheticals, which slightly hurts scannability.

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?

Covers the aggregation semantics and partial-data edge cases, and an output schema exists so return shape needn't be spelled out. Only gaps are parameter details and error behavior for an unknown package.

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 coverage is 0%, so the description must carry the parameter burden. It clarifies `days` semantics ('up to 100 days') but never states the default of 30 or gives any guidance on the `package` parameter format (exact name, case sensitivity). Partial compensation only.

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?

States a specific verb and resource ('Fetch daily download counts for a PyPI package') plus the window ('up to 100 days'). It is instantly distinguishable from the npm sibling and from the comparison tool.

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

Usage Guidelines5/5

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

Explicitly says when to use it ('when the user asks how a PyPI package is growing') and names the alternative with the condition that selects it ('for side-by-side comparison use `get_package_comparison`'). No inference required.

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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