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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

A4.9/5.0
Behavior5/5

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

Annotations already cover readOnly/idempotent/destructive safety. The description adds rich behavioral context: data sources (SEC EDGAR/XBRL, FAERS, FDA), correct handling of off-calendar fiscal years, sorting by primary metric, and the return format with pipeworx:// citation URIs per entity.

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

Conciseness5/5

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

The description is dense yet well-organized. It opens with query triggers, then explains the core parallel-call nature, then type-specific details, sorting, and output. Every sentence earns its place, with no redundant filler.

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?

Despite having no output schema, the description covers purpose, usage guidance, data sourcing, sorting behavior, and return format. For a comparison tool with two modes, it leaves no significant gaps for correct selection and invocation.

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?

The schema already covers parameter descriptions at 100%. The description adds type-specific behavior ('type=company pulls...' vs 'type=drug pulls...') and clarifies values format, going beyond schema. However, the schema already documents the ticker/name examples, so the added semantic value is useful but not transformative.

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 performs side-by-side comparison of 2-5 companies or drugs in a single parallel call. It includes concrete query examples and explicitly distinguishes itself from sequential single-entity lookups, saying 'ALWAYS PREFER over sequential single-pack lookups when comparing entities'.

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?

Provides explicit when-to-use guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities'. It also gives example trigger phrases and notes it 'Replaces 8–15 sequential lookups', making the intended usage context unmistakable.

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

A3.7/5.0
Disambiguation3/5

The tool set mixes two unrelated domains (Star Wars and Pipeworx data services), which is initially confusing. Within the Pipeworx suite, tools like ask_pipeworx and ask_pipeworx_grounded are clearly differentiated, but some overlap exists (e.g., deep_research vs. compare_entities both do multi-source lookups). Overall, most tools have distinct purposes, but the domain mismatch lowers clarity.

Naming Consistency4/5

All tools use snake_case consistently (e.g., ask_pipeworx, entity_profile, resolve_entity). The naming pattern is mostly verb_noun or descriptive_compound, which is predictable. Minor deviation: some tools start with a verb (ask_pipeworx) while others start with a noun (entity_profile), but the style is uniform.

Tool Count3/5

34 tools is on the high side but not unreasonable for a data-heavy server. However, the set covers two distinct domains (Star Wars and Pipeworx), making it feel bloated. The count could be reduced by separating the domains or pruning rarely-used tools.

Completeness3/5

The Pipeworx side appears comprehensive, covering queries, profiles, comparisons, subscriptions, alerts, and memory. The Star Wars side is incomplete—it lacks tools for vehicles, species, or individual characters (only search_people exists). The overall surface has gaps in one of its two domains.

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