recentFetch total PyPI download count for a package over a recent period (day, week, or month). Returns a single aggregate count from pypistats.org.
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | day | week | month | |
| package | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | ||
| type | No | Response type | |
| package | No | Package name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior. Description adds that it queries pypistats.org and returns a single aggregate count, providing useful external context.
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?
Single sentence of 22 words, front-loaded with verb and resource. No wasted 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?
With an output schema present, description need not detail return structure. It states returns a single aggregate count, which suffices. No mention of errors or limits, but acceptable for this low-complexity tool.
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 has 50% description coverage (period gets a description, package does not). Description adds meaning for 'package' by stating it fetches count for a package, and clarifies period values as day, week, month. This compensates for schema gaps.
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?
Description specifies verb 'fetch', resource 'total PyPI download count for a package', and scope 'over a recent period (day, week, or month)'. Clearly distinguishes from siblings like 'overall' which likely covers all-time 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?
Implies usage for recent period data but does not explicitly state when to use this vs alternatives, nor does it provide exclusions or prerequisites.
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.