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Glama

Weatherapi

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

Schema Changelog

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

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive. The description adds significant behavioral detail: parallel fan-out to multiple sources, fallback from GDELT to GNews on rate limits/5xx, USPTO soft-failure until reactivated, and structured return format with 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured: common query examples front-loaded, followed by data sources, parameter details, and return format. No redundant sentences. Every sentence contributes meaningful information.

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?

Despite no output schema, the description fully explains the return structure (changes grouped by source, total_changes count, citation URIs). It covers fallback mechanisms and limitations. For a tool with 3 required parameters and multiple data sources, this is complete and self-contained.

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?

The input schema has 100% description coverage, but the description adds substantial value: explains that 'since' accepts both ISO dates and relative shorthand with examples, recommends '30d' for typical monitoring, and clarifies that 'value' can be ticker or CIK. This goes beyond the schema definitions.

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 it provides a change feed for a company, listing specific data sources (SEC EDGAR, GDELT/GNews, USPTO). It explicitly distinguishes from sibling tool entity_profile, which is used for static profiles. The purpose is unambiguous and well-defined.

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 provides explicit when-to-use guidance with common query examples ("What's new with X", "latest on Y"). It directly advises using entity_profile instead for static profiles, and explains fallback behavior between GDELT and GNews. This offers clear context for appropriate tool selection.

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

All Weatherapi toolsInspect
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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