Look up Node.js package information from NPM registry. Returns latest version, download statistics (weekly/monthly), dependency list, package description, license, and GitHub link. Use for evaluating JavaScript libraries, checking maintenance status, or reviewing package popularity.
| Name | Required | Description | Default |
|---|---|---|---|
| package_name | Yes | NPM package name exactly as published (e.g. 'express', 'react', 'lodash', '@babel/core') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, but the description adds valuable behavioral context by enumerating exactly what data is returned (latest version, download stats, dependencies, license, GitHub link) and that it fetches from the NPM registry. It does not mention error handling or rate limits, but the read-only nature and return payload are well disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: the first states the core action and return contents, the second gives usage scenarios. Every word earns its place with no redundancy, and the most important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only lookup tool with one parameter and no output schema, the description covers the essential behavior and return fields. It is sufficient for an agent to select and invoke the tool, though it could mention handling of nonexistent packages or network failures.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage for the single parameter, fully explaining that package_name must be the exact published name with examples. The description does not add further parameter-specific meaning, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool looks up Node.js package information from the NPM registry, specifying the exact resource and action. It distinguishes itself from siblings like get_pypi_package and get_github_repo by focusing on npm packages and lists specific return data (version, downloads, dependencies, license, etc.).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit use cases: evaluating JavaScript libraries, checking maintenance status, and reviewing package popularity. It does not name alternatives or state when not to use it, but the context is clear relative to sibling tools. This is a strong 'when to use' statement, lacking only explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource and action: get for specific GitHub repos and npm/PyPI packages, search for GitHub repos, arXiv papers, Google Scholar papers, and Stack Overflow Q&A. No two tools overlap in purpose.
All tool names follow a consistent verb_source pattern using snake_case (get_github_repo, get_npm_package, search_arxiv, etc.). This makes the set predictable and easy to navigate.
With 7 tools, the server covers a focused domain—developer research and resource evaluation—without bloat. Each tool contributes a distinct function, and the count is well-balanced for the scope.
The tool surface covers fetching metadata for known repos/packages and searching multiple external platforms. A minor gap is the lack of direct search for npm or PyPI packages, but GitHub search partially fills this need. Overall, lifecycle coverage is appropriate for a read-only research assistant.