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Ipma Pt

by io.github.pipeworx-io
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Ipma Sea Forecast

ipma_sea_forecast
Read-onlyIdempotent

Sea state forecast for the Portuguese coast from IPMA oceanography — significant wave height (m), wave period (s), wave direction, and sea surface temperature (°C) for 12 coastal zones on the mainland, Madeira, and Azores. Good for surf, sailing, and beach conditions. Example: ipma_sea_forecast({ city: "Sagres" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dayNoForecast day: 0 = today (default), 1 = tomorrow, 2 = day after.
cityNoOptional coastal zone filter, e.g. "Sagres", "Lisboa", "Porto", "Funchal", "Ponta Delgada". Omit for all 12 zones.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "city": "Sagres",
+    "day": 0
+  },
+  {
+    "city": "Funchal"
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Adds beyond annotations: specifies exact metrics returned (wave height, period, direction, SST) and geographic coverage (12 coastal zones, mainland, Madeira, Azores). Annotations already declare read-only, idempotent, non-destructive; description complements well.

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?

Two sentences plus an example, no wasted words. Front-loaded with key purpose, then details and example.

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?

Returns specific quantitative metrics (with units), covers all needed context for agent to select and invoke. No output schema needed as description explains return values adequately.

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 covers both parameters with full description. Description adds contextual meaning: 'coastal zone filter' and example usage, plus info on omitting city for all zones. Baseline 3, description adds value.

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?

Clearly states it provides sea state forecast (significant wave height, wave period, wave direction, sea surface temperature) for Portuguese coast, covering specific zones. Distinguishes from sibling tools like ipma_forecast (weather) and ipma_uv_forecast.

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

Usage Guidelines4/5

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

Mentions it's good for surf, sailing, and beach conditions, and gives an example call. Does not explicitly state when not to use or provide alternatives, but context from siblings implies differentiation.

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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