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Armigerous

Dependency Freshness MCP Server

by Armigerous

Check dependency freshness

check_dependency_freshness

Check npm or PyPI packages for outdated versions, deprecation status, and cited breaking-change summaries since your current version, supporting time-sensitive AI agents.

Instructions

For each npm or PyPI package, determine whether it is outdated (out of date) and return its current version, release dates, deprecation status, how many stable versions you are behind, and a DATED, CITED "what changed since your version" breaking-change summary. Built for time-blind AI agents whose training cutoff makes them emit deprecated dependency code. Every answer carries source URLs + access dates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packagesYesPackages to check. Each: { ecosystem: "npm"|"pypi", name, currentVersion? }. currentVersion is the version you are assuming/about to use — supply it to get the "behind by N / changed since" diff.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorsYes
resultsYes
Behavior4/5

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

No annotations provided, but description discloses that it returns source URLs and access dates, and that it is a live check. Does not elaborate on potential latency or rate limits, but overall adequate.

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?

Two sentences with no wasted words. Front-loaded with core purpose followed by context and guarantees.

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

Completeness5/5

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

Given the tool's complexity and presence of an output schema, the description fully covers what it does, why it's needed, and what it returns.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and description adds value by explaining the purpose of 'currentVersion' (to get diff) and giving an example of the package object.

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?

Description clearly states verb 'check', resource 'dependency freshness', and specific outputs like current version, deprecation status, breaking-change summary. It is distinct and comprehensive.

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

Usage Guidelines4/5

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

Explicitly states it is built for time-blind AI agents to avoid deprecated code, providing clear context. No exclusions or alternatives needed due to lack of siblings.

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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