Skip to main content
Glama

Meteostat

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

A4.8/5.0
Behavior5/5

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

Annotations already indicate safe, idempotent behavior. Description adds multi-source fanout, soft-fail for USPTO, return structure, and parameter format details. 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 well-structured with examples upfront, but relatively long. Each sentence adds value, though minor redundancy (e.g., repeating fallback detail).

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 complexity (multi-source, fallback, parameter formats, no output schema), the description covers all necessary aspects: sourcing, parameter usage, return structure, and limitations (PatentsView sunset).

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 description is not required but adds value: explains acceptable formats for `since` (ISO date, relative shorthand) and recommends '30d' or '1m' for monitoring. Also clarifies `type` and `value` examples.

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 defines the tool as providing a change feed for a company (SEC filings, news, patents) over a time window, with specific query examples. It distinguishes from entity_profile, which is for static profiles.

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?

Explicitly states when to use (recent changes/news) and when not (static profile, redirecting to entity_profile). Also explains fallback behavior between GDELT and GNews.

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

All Meteostat toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

The set contains multiple overlapping families: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language data questions, while five polymarket tools and ai_visibility_check/scan_competitor_ai_presence further blur boundaries. The lengthy descriptions help, but an agent will still face genuinely ambiguous selection decisions across these clusters.

Naming Consistency2/5

Tool names mix verb-first patterns (find_stations, validate_claim), domain-first names (polymarket_edges, entity_profile), and brand-prefixed meta tools (pipeworx_trending, pipeworx_feedback). Snake_case is consistent, but there is no predictable verb_noun convention across the set, making the overall naming scheme feel more like a platform catalog than a coherent API.

Tool Count2/5

At 34 tools, this is too many for a well-scoped server, and most of the surface (prediction markets, AI visibility, npm scanning, llms.txt generation) is unrelated to the server's stated Meteostat identity. The count is driven by broad meta wrappers and overlapping data-access aggregates rather than a focused domain model.

Completeness2/5

For a server named Meteostat, the weather surface is notably incomplete: find_stations plus get_daily_history and get_monthly_normals covers stations, daily records, and normals, but there is no current-conditions, forecast, or hourly-history tool even though hourly availability is mentioned in station inventories. The rest of the set is a broad data-research layer, but it does not form a complete lifecycle for any single resource.

Resources