python_majorFetch PyPI download breakdown for a package by Python major version (2 vs 3) from pypistats.org. Returns per-version download counts.
| Name | Required | Description | Default |
|---|---|---|---|
| package | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | Downloads by Python major version | |
| 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, idempotentHint=true, and destructiveHint=false, covering safety. The description adds that data is sourced from pypistats.org and returns per-version counts, providing useful context beyond annotations.
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 a single sentence that efficiently communicates the tool's purpose and output. No unnecessary words; 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?
Given the low complexity (one parameter, output schema exists), the description is complete. It explains what the tool does and what it returns. No missing information needed for an agent to invoke it correctly.
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 schema provides no description for the 'package' parameter. The description only implies it is the package name through the example. With a single required parameter, the meaning is inferred, but the description does not explicitly state format or constraints.
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 explicitly states the action ('Fetch PyPI download breakdown'), the resource ('package by Python major version'), and the data source ('pypistats.org'). It also mentions the return type ('per-version download counts'). This clearly distinguishes from sibling tool 'python_minor' which would handle minor versions.
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 major version breakdown, but does not explicitly state when to use this tool over alternatives like 'python_minor'. No when-not-to-use or exclusion criteria are provided.
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.