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Arcgis Lubbock

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

A5/5.0
Behavior5/5

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

Annotations declare readOnlyHint and non-destructive, so the description adds rich behavioral detail: data sources (SEC EDGAR/XBRL, FAERS), specific metrics per type, sorting by primary metric, and return format with citation URIs. This goes far beyond the annotations.

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 but every sentence earns its place: trigger phrases, preference rule, data specifics, sorting behavior, return format, and replacement count. No filler or redundancy.

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?

With no output schema, the description explains return values ('paired data + citation URIs'). It covers both entity types, sorting, fiscal year handling, and the efficiency gain, making it complete for invocation.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds significant meaning by explaining what each type retrieves (financial metrics vs FAERS/FDA/trial counts) and gives examples of valid values. This helps the agent select appropriate parameters.

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 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call' – a specific verb+resource. It lists trigger phrases and contrasts with sequential lookups, distinguishing it from siblings like entity_profile.

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 says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and provides clear trigger examples. It also differentiates company vs drug use cases, giving strong when-to-use 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

A3.8/5.0
Disambiguation2/5

Many tools overlap in purpose, particularly the ask_pipeworx variants (stable, beta, grounded) and deep_research, making it difficult for an agent to choose correctly. Additionally, the Arcgis-specific tools are buried under numerous general Pipeworx tools.

Naming Consistency4/5

Most tools follow a verb_noun pattern (e.g., ask_pipeworx, compare_entities), but there are minor inconsistencies like ai_visibility_check vs scan_competitor_ai_presence and the simpler Arcgis tool names.

Tool Count2/5

With 34 tools, the server is overloaded, especially since the majority are Pipeworx meta-tools unrelated to the Arcgis Lubbock purpose. A well-scoped ArcGIS server would have far fewer tools.

Completeness2/5

For an ArcGIS server, only three tools directly serve that purpose (search_datasets, layer_info, query_layer), lacking editing, analysis, or visualization capabilities. The remaining tools address a completely different domain.

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