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Arcgis Tucson

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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds valuable behavioral details: pulls latest 10-K for companies, handles off-calendar fiscal years, returns sorted results, and includes 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?

Description is fairly long but informative. It starts with user-facing example queries, then explains behavior and data sources. Every sentence adds value, though some redundancy exists (e.g., repeating entity count limits already in schema). Well-structured and front-loaded.

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 complexity of comparing entities and the lack of output schema, description provides complete context: data sources (SEC EDGAR, FAERS), handling of fiscal year differences, sorting behavior, and return format (paired data + citation URIs). Annotations cover safety concerns, so no gaps remain.

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. Description adds meaning beyond schema: explains that 'company' type pulls revenue/net income/cash/debt from SEC EDGAR/XBRL, and 'drug' type pulls FAERS adverse-event counts. It also clarifies that values should be tickers/CIKs for companies and drug names for drugs, with min/max constraints repeated.

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 explicitly states side-by-side comparison of 2-5 companies or drugs, with example queries like 'X vs Y' and 'rank these companies'. It clearly distinguishes from sibling tools by stating 'ALWAYS PREFER over sequential single-pack lookups'. Verb+resource is specific.

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?

Description explicitly says when to use (comparing entities head-to-head) and when not to (avoid sequential lookups). It provides concrete examples of queries and explains behavior for 'largest'/'most' queries, guiding the agent on input phrasing.

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

While individual tool descriptions are detailed and specific, the set includes overlapping tools like ask_pipeworx and ask_pipeworx_grounded, and several prediction market tools with similar purposes (bet_research, polymarket_edges, polymarket_arbitrage). The broad range of unrelated domains means many tools are distinct, but some pairs are ambiguous.

Naming Consistency2/5

Tool names use snake_case but follow no consistent pattern. Some are verb_noun (ask_pipeworx, query_layer), some noun_verb (ai_visibility_check, entity_profile), and some have inconsistent structure (discover_tools, recent_alerts). The mix of conventions reduces predictability.

Tool Count1/5

33 tools is excessive for a server named 'Arcgis Tucson', as only 3 tools relate to ArcGIS (search_datasets, layer_info, query_layer). The rest span completely unrelated domains (Pipeworx data, Polymarket, memory, npm scanning, etc.), creating a severe mismatch between server name and tool functionality.

Completeness3/5

Considering the actual tool surface (a diverse data retrieval and prediction market analysis set), it is reasonably complete for common lookups (SEC, FDA, economics, news, bets). However, it lacks web search and the ArcGIS tools are minimal. The absence of a cohesive domain makes completeness hard to judge, but for the implied data-retrieval purpose, it's passable.

Resources