[Developer Tools] Get Python package details from PyPI — version, downloads, dependencies, and description.
Args: package_name: PyPI package name (e.g. 'requests', 'pandas', 'fastapi')
| Name | Required | Description | Default |
|---|---|---|---|
| package_name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds value by specifying what data is returned (version, downloads, dependencies, description), which is beyond the annotations. No contradictions.
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 very concise, using a single line for purpose and a short Args section. Every sentence is informative and 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 1-parameter tool with no output schema, the description is largely complete. It mentions the return data but could note potential error handling (e.g., missing package). Still adequate.
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?
With 0% schema description coverage, the description compensates by explaining the parameter 'package_name' as 'PyPI package name' and providing examples. This adds crucial meaning beyond the bare schema type.
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's purpose: 'Get Python package details from PyPI' and lists specific data returned (version, downloads, dependencies, description). It distinguishes itself from siblings like 'get_npm_package' by targeting PyPI packages.
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 implies usage for Python packages by providing example names and specifying PyPI, but it does not explicitly state when to use this tool over alternatives or provide any exclusion criteria. No guidance on when not to use it.
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.
Many tools have overlapping functionality across categories (e.g., multiple search_arxiv, search_google_scholar, real estate tools, DNS/WHOIS checks). An agent would struggle to differentiate between similar tools from different categories, leading to ambiguity.
Tools follow a 'category__verb_noun' pattern mostly, but verbs vary (get, search, screen, check, etc.) and some categories use different orders (e.g., 'get_repo_stats' vs 'search_repos'). The consistency is acceptable but not uniform across the entire set.
With 152 tools, the server is excessively large for a single MCP server. While it aims to be an all-in-one gateway, the sheer number overwhelms the agent and likely exceeds practical limits for coherent selection.
The server covers a wide range of domains (finance, real estate, news, developer tools, etc.) but has notable gaps (e.g., social media APIs, CRM tools). Coverage is broad but not exhaustive, and some niche areas (e.g., global stock exchanges) are over-represented.