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Weatherapi

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"]).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already show readOnlyHint, openWorldHint, idempotentHint. The description adds specific data sources (SEC EDGAR/XBRL, FAERS), explains handling of off-calendar fiscal years, sorting by primary metric, and return format (paired data + 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 a single dense paragraph but every sentence adds value. It could be slightly more structured with bullet points for clarity, but it is not overly long. Minor deduction for lack of visual structure.

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 a comparison tool with two entity types, multiple data sources, and sorting, the description covers what the tool does, how it works, and what to expect. It explains return format despite no output schema. Complete enough for an agent to use correctly.

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% (both parameters described), baseline 3. The description adds significant value by explaining each enum value ('company' pulls 10-K data, 'drug' pulls FAERS counts) and providing concrete examples for the values array (tickers/CIKs for companies, drug names). This goes far beyond 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 performs side-by-side comparison of 2-5 companies or drugs. It differentiates from siblings by explicitly preferring over sequential single-pack lookups and claims to replace 8-15 lookups. Natural language triggers like 'compare X and Y' make the purpose unmistakable.

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?

Provides explicit when-to-use guidance ('ALWAYS PREFER over sequential single-pack lookups'), natural language triggers, and details on what each entity type returns (SEC data for companies, FAERS data for drugs). It contrasts with the sibling entity_profile tool by emphasizing parallel vs. sequential calls.

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

Several tools have near-identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly described as functionally identical, and six Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread, polymarket_edge_tracker) overlap heavily in discovery, edge, and arbitrage roles. Company-research tools (entity_profile, compare_entities, recent_changes) also blur boundaries, making misselection likely.

Naming Consistency3/5

All names use lowercase snake_case with underscores, which is a consistent base convention. However, the lexical pattern varies: bare single words (current, forecast, remember, forget) coexist with verb_noun compounds (resolve_entity, validate_claim) and noun compounds (entity_profile, polymarket_edges). The lack of a uniform verb_noun structure makes the set less predictable, though still readable.

Tool Count1/5

35 tools is well into the 'too many' range, and the server's name promises weather while only 4 of 35 tools (current, forecast, astronomy, marine) are weather-related — an extreme mismatch between the declared purpose and the actual surface. The remaining 31 tools form a general data/prediction-market platform that would be better served under a different server name.

Completeness4/5

Judged by its actual (non-weather) domain, the set is quite complete: generic routed lookup, grounded answer mode, deep research, entity resolution/profile/comparison, claim validation, subscriptions, memory, discovery, and feedback are all present. The weather subset covers current conditions, forecasts, marine, and astronomy, though it lacks historical weather and alert endpoints — a minor gap.

Resources