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Ipma Pt

by io.github.pipeworx-io

Ipma Forecast

ipma_forecast
Read-onlyIdempotent

Portugal weather forecast — official IPMA 5-day forecast for a Portuguese city: Lisbon, Porto, Faro, the Algarve, Madeira (Funchal), Azores (Ponta Delgada). Returns daily min/max temperature (°C), precipitation probability, weather description in English, and wind direction/strength. Example: ipma_forecast({ city: "Lisboa" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYesPortuguese city or island, e.g. "Lisboa", "Lisbon", "Porto", "Faro", "Funchal", "Ponta Delgada". Accent-insensitive; use ipma_locations to browse all 35.

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: +[
+  {
+    "city": "Lisboa"
+  },
+  {
+    "city": "Porto"
+  }
+]
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description does not need to repeat safety. It adds behavioral details: returns multiple weather metrics, example usage, and that it is official IPMA data. 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.

Conciseness5/5

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

The description is two concise sentences plus an example. It front-loads the purpose and includes an example call, making it efficient and easy to parse.

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?

With only one parameter and no output schema, the description comprehensively explains what is returned (temperature, precipitation, wind, etc.), making it complete for an agent to understand the tool's behavior.

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?

The schema already covers the city parameter with examples and a tip to use ipma_locations. The description adds examples and notes accent-insensitivity, which provides extra context beyond the schema.

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 returns the official IPMA 5-day forecast for a Portuguese city, listing specific cities and the data provided (min/max temp, precipitation, etc.). It distinguishes itself from sibling tools like ipma_sea_forecast and ipma_seismic by focusing on city weather.

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?

The description implies usage for city weather forecasts and provides an example call. It does not explicitly state when not to use it or mention alternatives, but the sibling tool names and context make the purpose clear.

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

A3.9/5.0
Disambiguation2/5

The ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded / deep_research cluster overlaps heavily on question-routing, and the six polymarket_* tools all operate on prediction-market edges and can be confused. The ipma_*, memory, and subscription tools are distinct, but the overlapping clusters create real misselection risk.

Naming Consistency3/5

Snake_case is used throughout and there are clear prefix groups (ipma_*, ask_pipeworx, polymarket_*), but the rest mix verb-first (compare_entities, discover_tools), noun-first (entity_profile, bet_research), and bare verbs (remember, recall, forget). Readable overall, but no consistent verb_noun convention.

Tool Count2/5

36 tools is heavy, and the server name 'Ipma Pt' implies a narrow Portugal-weather service while 31 of the tools belong to a broad Pipeworx data/prediction-market platform. The scope mismatch makes the count feel bloated rather than curated.

Completeness4/5

The dominant Pipeworx domain is well covered: querying, grounded answers, deep research, entity resolution, comparison, claim validation, subscriptions, memory, and tool discovery are all present with few dead ends. Minor gaps exist (e.g., no general web search tool, thin IPMA historical/warning coverage), but agents can work around them.

Resources