get_package_versionGet metadata for a specific version of a Python package on PyPI. Returns the summary, required Python version, the full dependency list (requires_dist, i.e. what pip would resolve), and the downloadable files for that version. Use to inspect a pinned release like requests 2.31.0.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Exact PyPI package name, e.g. "requests". | |
| version | Yes | Version string, e.g. "2.31.0". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "name": "requests",
+ "version": "2.31.0"
+ },
+ {
+ "name": "django",
+ "version": "4.2.0"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, idempotent, openWorld, and non-destructive behavior. The description adds value by listing the specific data returned (summary, Python version, dependency list, downloadable files), which gives the agent a clear expectation of the response content.
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 three concise sentences: one for purpose, one for contents, one for usage example. Every sentence is meaningful and well-structured, with no unnecessary 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 no output schema, the description adequately explains what is returned. It does not mention error cases or limitations, but for a simple metadata lookup with strong annotations, it is sufficiently complete.
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?
The input schema already has 100% coverage with clear descriptions for both parameters. The description does not add additional semantics or constraints beyond what the schema provides, so it meets the baseline without extra value.
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 it retrieves metadata for a specific version of a Python package from PyPI, with a list of returned fields. It is explicit about the verb and resource, but does not explicitly differentiate from sibling tools like 'get_package' or 'list_releases', though the version-specific nature is implied.
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 a usage example ('Use to inspect a pinned release like requests 2.31.0'), indicating when to use it. However, it does not mention when not to use it or point to alternative tools for other scenarios (e.g., getting latest version).
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.
Several tools have overlapping or near-identical purposes: ask_pipeworx and ask_pipeworx_beta are currently exactly the same behavior, and polymarket_edges, polymarket_arbitrage, and bet_research all present as 'find a betting opportunity' scanners. The detailed descriptions help, but an agent still needs careful triage to avoid picking the wrong entry point.
The set is uniformly snake_case and many tools follow verb_noun (get_package, list_releases, resolve_entity), but conventions are split between product-prefixed families (ask_pipeworx, pipeworx_feedback, polymarket_*), noun-led names (entity_profile, recent_alerts, deep_research), and bare-verb memory tools (remember, recall, forget). The result is readable but not a single predictable pattern.
35 tools is well into the overgrown range, and the count is especially mismatched for a server labeled Pypi: only a handful of tools actually relate to Python packages, while the rest cover Pipeworx data lookup, prediction markets, AI visibility, memory, and subscriptions. This looks like several unrelated tool surfaces merged under one server.
For the broad Pipeworx data-research surface, coverage is strong: lookup, grounded answers, deep research, entity profiles, comparisons, validation, subscriptions, and memory are all represented. However, for a PyPI-focused server the surface is incomplete—there is no package search, upload, or account/maintainer tooling—and the PyPI tools feel like an afterthought.