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Named Package Comparison

compare_competitors
Read-onlyIdempotent

Compare live npm or PyPI metadata for two or more exact package names side by side to verify claims about recency, maintenance status, or licenses.

Instructions

Compare two or more exact package names side by side using live npm or PyPI metadata. Use this when you already know the candidate packages and need evidence for claims such as 'tool A is newer', 'tool B is still maintained', or 'these packages use different licenses'. It returns per-package registry metadata in input order, with field availability varying by registry. Missing or unpublished packages return found=false. Do not use it to discover unknown alternatives, estimate market size, or compare packages across different registries. Registry responses are cached for 5 minutes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packagesYesTwo to ten exact package names from the same registry, for example ['react', 'vue']. Use exact registry names, not search phrases or categories.
registryNoRegistry that all package names belong to. All compared packages must come from the same registry, and returned metadata fields differ slightly between npm and PyPI.npm

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
packagesYesPackage names that were requested for comparison.
registryYesRegistry used for all comparisons.
comparisonsYesPer-package lookup results returned in the same order as the input package list. Some fields only exist for npm or only for PyPI, so consumers should treat absent fields as normal.
Behavior5/5

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

Discloses return format (per-package registry metadata in input order), field variability by registry, handling of missing packages (found=false), and caching (5 minutes). Annotations already show read-only, non-destructive, idempotent, open-world; description adds useful context.

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?

Four sentences, front-loaded with purpose, then use cases, output behavior, and caching/exclusions. No wasted words, well-organized.

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?

With 2 parameters, output schema, rich annotations, and description covering inputs, outputs, edge cases (missing packages), prohibitions, and caching, the tool is fully specified for an AI agent.

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 covers both parameters with descriptions; description reinforces with examples ('react', 'vue') and constraints (exact names, same registry). Since schema coverage is 100%, baseline is 3; description adds marginal extra meaning.

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 the verb 'compare' and resource 'exact package names using live npm or PyPI metadata'. It differentiates from sibling tools like estimate_market by focusing on known packages, not discovery.

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 states when to use (known candidates needing evidence for claims like newer, maintained, different licenses) and when not to use (discovery, market size, cross-registry). Provides clear exclusions.

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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