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Va Museum

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds value by detailing the data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), sorting by primary metric, and return format (paired data + citation URIs). No contradictions.

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 efficiently written, front-loading trigger phrases and key guidance. Every sentence serves a purpose, though it could be slightly more structured for readability. Overall, it's concise and informative.

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 the tool's moderate complexity (two entity types, multiple data fields), the description covers all essential aspects: data sources, fiscal year handling, sorting, output format, and efficiency benefit. It leaves no critical gaps for an AI agent to invoke the tool correctly.

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% with both parameters described. The description enhances understanding by explaining the effect of 'type' (company vs drug) and giving concrete examples for 'values' (tickers, drug names, constraints). This adds meaning beyond the JSON schema.

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 explicitly states the tool's purpose: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It distinguishes from siblings by noting 'ALWAYS PREFER over sequential single-pack lookups,' which contrasts with tools like entity_profile that provide single-entity lookups.

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 provides clear usage cues with trigger phrases ('Compare X and Y', 'X vs Y', etc.) and explicitly recommends this tool over sequential lookups for comparisons. It lacks specific exclusions but adequately guides when to use it.

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

All Va Museum toolsInspect
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TDQS

A3.7/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is notable overlap between ask_pipeworx and ask_pipeworx_grounded (same underlying data query, different answer modes), and multiple polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges) can confuse agents about which to use for a given betting query. Memory tools (remember/recall/forget) are clear, but the mix of museum, financial, and prediction market tools under one server increases ambiguity.

Naming Consistency3/5

All tool names use snake_case consistently, but the naming pattern is inconsistent: some start with a verb (search_objects, list_subscriptions, remember) while others start with a noun or modifier (ai_visibility_check, entity_profile, polymarket_arbitrage). The 'ask_' prefix is used twice, but overall there is no single predictable convention like verb_noun across the set.

Tool Count3/5

29 tools is high but not excessive for a general-purpose data server. However, the server is named 'Va Museum' which implies a narrow domain, making the count seem bloated. The set includes many tools unrelated to a museum (e.g., prediction markets, SEC filings), so the count is appropriate only if the server's actual scope is broad and multi-domain.

Completeness2/5

For a museum-focused server, the tool surface is severely incomplete, with only two museum-specific tools (search_objects, get_object) out of 29. Even as a general-purpose server, it lacks tools for common operations like updating or deleting resources, and the coverage of domains (e.g., no tool for creating or managing user data) feels ad hoc rather than systematically complete.

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