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Glama

Open Meteo

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

A4.5/5.0
Behavior4/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: parallel fan-out across sources, fallback logic, return structure. 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, well-structured paragraph. It front-loads query examples, then defines the tool's scope and behavior. Every sentence adds necessary context without repetition.

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?

Despite no output schema, the description explains the return format (changes[] grouped by source, total_changes, citation URIs). Parameters are fully documented, and the tool's complexity is adequately addressed.

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 baseline is 3. The description adds value by explaining the since format with examples (ISO date, relative shorthand) and suggesting typical monitoring windows. It also clarifies the value parameter accepts ticker or CIK.

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 identifies the tool as a change feed for a company, querying multiple sources (SEC EDGAR, GDELT/GNews, USPTO). It explicitly distinguishes from the sibling tool entity_profile, which handles 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?

The description provides explicit query examples and states when to use entity_profile instead. It also explains fallback behavior (GDELT→GNews) and soft-fail conditions (USPTO).

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

All Open Meteo toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

Many tools have overlapping purposes, such as ask_pipeworx vs ask_pipeworx_grounded and the multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research). While some tools are clearly distinct (e.g., geocode vs forecast), the high number of similar tools increases the risk of agent misselection.

Naming Consistency2/5

Naming conventions are inconsistent. Some tools use a consistent verb_noun pattern (e.g., ask_pipeworx, resolve_entity), while others have no prefix (remember, recall) or use a domain prefix (polymarket_arbitrage, pipeworx_feedback). The mix of styles and lack of a unified pattern reduces predictability.

Tool Count2/5

With 33 tools, the set is too large for a focused server, especially given the name 'Open Meteo' which implies weather tools only. Many tools are redundant or cover vastly different domains, making the count feel bloated and difficult for an agent to navigate efficiently.

Completeness4/5

The tool set covers an impressively wide range of capabilities: weather data, SEC filings, Polymarket analysis, memory management, and more. For the actual scope of data querying and analysis, there are few obvious gaps (e.g., no direct database query tool beyond ask_pipeworx). However, the completeness relative to the implied weather domain is poor.

Resources