systemFetch PyPI download breakdown for a package by operating system (Linux, Windows, Darwin, null) from pypistats.org. Returns per-OS download counts.
| Name | Required | Description | Default |
|---|---|---|---|
| package | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | Downloads by operating system | |
| type | No | Response type | |
| package | No | Package name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds value by specifying the external data source (pypistats.org) and the output structure (per-OS download counts), exceeding what annotations provide.
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 concise sentences, front-loaded with the core action and scope. No redundant words.
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?
Given the tool has an output schema (not shown), the description need not detail return format. It covers the source, action, and output structure. Could mention that the breakdown is by OS, but it already does.
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 coverage is 0%, meaning the description does not describe the 'package' parameter. While the description mentions 'a package', it does not explicitly state that the parameter is the package name or provide details. With only one parameter, more clarity is expected.
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 fetches PyPI download breakdown by OS from pypistats.org, with specific verb 'Fetch' and resource 'PyPI download breakdown for a package'. It distinguishes from sibling tools like python_major or python_minor by focusing on per-OS data.
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 does not provide explicit guidance on when to use this tool versus alternatives or when not to use it. The purpose is implied but not elaborated.
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.
There is significant overlap between tools like ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research, all performing similar data lookup functions. Additionally, multiple prediction market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk) have overlapping purposes. An agent would struggle to choose the correct tool without deep understanding of subtle differences.
Tool names follow a consistent snake_case pattern and use clear domain prefixes (ask_pipeworx, polymarket_, python_) and verb_noun structure (e.g., validate_claim, compare_entities). Minor inconsistency exists with tools like 'overall' not following verb_noun, but overall pattern is predictable.
With 36 tools, the count is high but justifiable given the broad array of capabilities (data queries, prediction markets, memory, subscriptions). However, the server name 'Pypi Stats' suggests a narrow focus, making the count feel excessive for that purpose. The actual scope is wide, so the count is borderline appropriate.
For its actual scope as a data query and analysis platform, the tool set is quite complete: it covers company profiles, comparisons, claim verification, trend analysis, and prediction market insights. Minor gaps exist (e.g., no update/delete for most data types), but core query and lookup operations are well covered.