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Pypi Stats

by io.github.pipeworx-io

Compare Entities

compare_entities
Read-onlyIdempotent

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valuesYesFor company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]).

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description details what data is fetched for each type (SEC filings for companies, FAERS for drugs), how fiscal years are handled, sorting by primary metric, and return of citation URIs. This provides excellent behavioral insight.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is packed with useful information in a single paragraph, but it is not overly long. It front-loads the purpose and usage triggers. A slightly more structured format (e.g., bullet points) could improve readability, but it's already efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and lack of output schema, the description adequately explains the inputs, data sources, and behavior (fiscal year handling, sorting). It doesn't detail the exact output structure, but it provides enough for an agent to decide correctness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the description still adds value by giving concrete examples (e.g., 'AAPL','MSFT' for companies, 'ozempic','mounjaro' for drugs) and explaining what data each type retrieves. This goes beyond the schema's basic type and constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It provides specific natural language triggers and distinguishes from sequential lookups, making the purpose immediately clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' giving clear usage guidance. It also explains when to use company vs drug type. However, it lacks explicit exclusions or when not to use it, but the context is strong.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0
Disambiguation2/5

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.

Naming Consistency4/5

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.

Tool Count3/5

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.

Completeness4/5

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.

Resources