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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

A4.8/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description details what data is pulled for each type (company: 10-K financials with fiscal year handling; drug: FAERS counts, FDA approvals, trials), result sorting, and citation URIs. No contradictions with annotations.

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 front-loaded with trigger phrases and functional explanation. While it is a bit verbose, every sentence adds value—no filler. Minor improvement could be to group related details more tightly.

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 no output schema, the description explains the return format (paired data + citation URIs) and result sorting. It covers both entity types, constraints (2–5 items), and edge cases (off-calendar fiscal years), making it complete for an AI agent.

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%, so baseline is 3. The description adds significant meaning by explaining what each 'type' provides (company vs drug) and giving concrete examples for 'values' (tickers/CIKs for companies, drug names). This exceeds mere repetition.

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 starts with clear trigger phrases ('Compare X and Y', 'X vs Y') and states the tool performs side-by-side comparison of 2–5 companies or drugs. It distinguishes itself from sibling tools by advising to 'ALWAYS PREFER over sequential single-pack lookups', making the 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?

The description explicitly tells when to use this tool (comparing 2–5 entities) and provides strong guidance to prefer it over sequential lookups. Examples of user queries ('which is bigger', 'head to head') further clarify usage context.

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

B3.3/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially the Pipeworx utilities (ask_pipeworx, ask_pipeworx_grounded, deep_research, etc.). The comic tools are distinct but the large number of similar generally-purpose tools creates confusion across the set.

Naming Consistency3/5

Comic tools follow a consistent noun pattern (character, characters, issue, issues) but Pipeworx tools mix verb_phrase (ask_pipeworx), noun_phrase (entity_profile), and composite names (scan_competitor_ai_presence). No single convention dominates.

Tool Count1/5

The server name 'Comicvine' suggests a focused comic book reference, yet 30 of 40 tools are unrelated Pipeworx services (company financials, prediction markets, subscriptions, etc.). This is a severe scope mismatch.

Completeness2/5

The comic-related tools cover characters, issues, volumes, publishers, and creators reasonably well, but the server's overall purpose is diluted by including many non-comic tools that don't form a coherent surface. The comic subset is complete, but the full set is not.