List recent released versions of a package with release dates (most recent first). Helps an agent pick a version or check how actively it is maintained. Ecosystems: npm, pypi, cargo.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Exact package name as published in that registry, e.g. express for npm, requests for pypi, serde for cargo. | |
| limit | No | Maximum number of results to return. | |
| ecosystem | Yes | Package registry to look in. One of: npm, pypi, cargo. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It discloses the ordering ('most recent first') and the inclusion of release dates, which is useful. However, it does not clarify whether prereleases are included, what 'recent' means, how pagination works, or the exact returned format beyond versions and dates.
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 with no filler. The core action and output ordering are front-loaded, and the second sentence adds practical purpose. Every sentence earns its place.
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 list tool with well-covered parameters, the description gives enough context to invoke it: what it lists, the output ordering, and the supported ecosystems. With no output schema, it could still mention the exact fields returned or edge cases around release types, but these are minor gaps.
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?
Schema description coverage is 100%, so the baseline is 3. The description adds ecosystem context by listing npm, pypi, and cargo, but this largely repeats the schema enum. It does not add significant meaning beyond what the input schema already provides.
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 uses a specific verb ('List') and resource ('recent released versions of a package with release dates') and states the ordering ('most recent first'). It clearly distinguishes this from sibling tools like package_dependencies, package_downloads, or package_info by focusing on version history and release dates.
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 clear use cases: 'pick a version' or 'check how actively it is maintained.' It does not explicitly name alternatives or state when not to use it, but the use-case context is strong enough for an agent to select this over siblings.
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 aspect of package intelligence: dependencies, download statistics, health, metadata, search, and version history. There is no overlap in purpose, and the descriptions clearly differentiate what each tool returns.
All tools follow the consistent pattern 'package_' plus a descriptive noun (dependencies, downloads, health, info, search, versions). The naming is uniform, snake_case, and immediately conveys each tool's function.
Six tools is well within the ideal 3–15 range and matches the server's single focus on package evaluation. Each tool covers a necessary facet without redundancy or bloat.
The tool surface covers the full lifecycle of package evaluation: discovering packages via search, inspecting metadata, checking version history, assessing popularity, and performing a composite health check including security advisories. No obvious gaps exist for the stated purpose.