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Glama

Hurricanes

by io.github.pipeworx-io

Active Storms

active_storms
Read-onlyIdempotent

List currently active tropical cyclones (tropical storms / hurricanes) from the US National Hurricane Center — name, classification, Saffir-Simpson category, position, max winds, pressure, movement, and links to official advisories/forecast cone. Filter by basin. Keyless. Returns an empty list in the off-season.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
basinNoOptional filter: "atlantic", "eastern-pacific", or "central-pacific".

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "basin": "atlantic"
+  },
+  {
+    "basin": "eastern-pacific"
+  }
+]
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds key facts: 'Keyless' (no auth needed) and 'Returns an empty list in the off-season', which provide behavioral context beyond the 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 concise, using bullet-like phrasing (dash-separated fields) and separate short sentences for filters, keyless, and seasonal behavior. Every sentence adds unique value with no redundancy.

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?

For a simple read-only listing tool with one optional parameter and no output schema, the description covers purpose, capabilities, and edge cases (off-season). Missing aspects like data freshness are minor given the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a clear description of the 'basin' parameter. The description only repeats 'Filter by basin', adding no new semantic value beyond what the schema provides.

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 explicitly states 'List currently active tropical cyclones' with a specific source (US National Hurricane Center) and enumerates the returned fields. It distinguishes from sibling tools by focusing on active storms listing, while 'storm_details' likely provides per-storm info.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for listing active storms and notes that empty list is returned in off-season. However, it does not explicitly compare to alternatives like storm_details or specify when not to use this tool.

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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes with clear descriptions, but ask_pipeworx_beta is nearly identical to ask_pipeworx, and the presence of several meta-tools may cause slight confusion. Overall, an agent can differentiate most tools.

Naming Consistency4/5

Tool names consistently use lowercase_with_underscores and follow an action_domain pattern (e.g., validate_claim, scan_competitor_ai_presence). There is a mix of verb-noun and noun-verb, but the pattern is predictable and readable.

Tool Count3/5

33 tools is on the higher end, but given the broad scope (finance, drugs, prediction markets, data retrieval, memory), the count is reasonable. However, the server name 'Hurricanes' suggests a narrower focus, making the count feel excessive for that domain.

Completeness4/5

The tool set covers a wide range of data domains and includes meta-tools for discovery, grounded answers, and subscriptions. Minor gaps exist (e.g., no non-US company data), but overall the surface is comprehensive for a general-purpose data server.

Resources