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Serpapi

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
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. Description adds details: parallel call, data sources (SEC EDGAR/XBRL, FAERS), citation URIs, and fiscal year handling. No contradiction.

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?

Description is information-dense but front-loaded with purpose. Every sentence adds value, though it could be slightly more concise. No wasted words, but slightly longer than minimal.

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

Completeness5/5

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

Given no output schema, description covers return format (paired data + citation URIs), sorting, data sources, and fiscal year handling. Sufficient for an agent to understand tool behavior and expected outputs.

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

Parameters4/5

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

Schema coverage is 100% but description adds examples and context: company expects tickers/CIKs, drug expects names, and explains what each type pulls. This goes beyond basic schema and helps correct parameter usage.

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?

Description clearly states the tool performs side-by-side comparison of 2-5 companies or drugs in one parallel call, with examples like 'Compare X and Y' and 'X vs Y'. It distinguishes from sequential lookups, making purpose unambiguous.

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

Usage Guidelines5/5

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

Explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', guiding when to use. Explains parameter differences for company vs drug, and results sorted by primary metric, which aids selection.

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

A3.8/5.0
Disambiguation3/5

Many tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, deep_research, and bet_research all querying data but with nuanced differences. The descriptions help but boundaries remain fuzzy, especially between ask_pipeworx and deep_research for broad vs. single lookups. Overall moderate ambiguity.

Naming Consistency2/5

Naming is inconsistent: some tools use snake_case (ask_pipeworx, ai_visibility_check), others use camelCase (serpapi_google_jobs), and patterns vary widely (e.g., pipeworx_feedback vs. compare_entities). Only the serpapi_google_* group follows a consistent pattern.

Tool Count3/5

36 tools is on the high side for a single server, with many meta-tools (discover_tools, suggest_questions) and niche prediction market tools. The scope seems overly broad, covering data lookup, prediction markets, memory, and subscriptions, which could be streamlined to a more focused set.

Completeness3/5

The server covers a wide range of domains (financial, economic, news, drugs, prediction markets, Google services), but lacks direct web search and write/update capabilities. While the coverage is broad, there are notable gaps (e.g., no generic web search, limited tool for modifying data) for a data-focused server.

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