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Glama

Weather Gc Ca

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

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds rich behavioral details: data sources (SEC EDGAR/XBRL, FAERS, FDA), handling of off-calendar fiscal years, 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 dense but well-organized with examples and explicit instructions. While it could be slightly more concise, every sentence adds meaningful information. The front-loading of example queries helps quick understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's complexity: two entity types, multiple data sources, sorting, and response format. Even without an output schema, it explains what is returned (paired data + citation URIs). No major gaps.

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 the schema already documents the parameters. The description adds value by explaining what data is pulled for each entity type (e.g., 'latest 10-K revenue + net income' for companies) and the max/min items, enriching the 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 clearly states the tool does side-by-side comparison of 2-5 entities (companies or drugs), pulling specific financial or regulatory data. It distinguishes itself from sibling tools like entity_profile (single entity) and search_within (different purpose).

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 gives explicit guidance to 'ALWAYS PREFER over sequential single-pack lookups' and provides example queries. It lacks explicit 'when not to use' instructions but is strong on positive 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

A3.8/5.0
Disambiguation3/5

Many tools have distinct purposes (e.g., current_observations vs. climate_daily), but several query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, bet_research) overlap in function, all routing questions to a large tool catalog. This can confuse an agent about which to use.

Naming Consistency3/5

Names are snake_case but follow no consistent pattern: some are verb_noun (ask_pipeworx), some noun_adjective (climate_daily), others compound (ai_visibility_check). The mix is readable but not predictable.

Tool Count2/5

33 tools is high for a server named 'Weather Gc Ca', which implies a focused weather service. Many tools are unrelated to weather (Polymarket, SEC, FDA, etc.), making the count inflated and mismatched to the server's apparent scope.

Completeness2/5

For weather, the server covers alerts, current observations, and climate records but lacks forecasts, radar, satellite imagery, and station listings. While the broader Pipeworx catalog is extensive, the weather-specific surface is incomplete for a dedicated weather tool.

Resources