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ph-civic-data-mcp

by xmpuspus
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Philippine weather forecast

get_weather_forecast
Read-onlyIdempotent

Get weather forecasts for Philippine cities, provinces, or municipalities. Uses PAGASA when available, otherwise Open-Meteo.

Instructions

Get the weather forecast for a Philippine location.

Uses the PAGASA TenDay API when PAGASA_API_TOKEN is set, and falls back to Open-Meteo when the token is absent or the PAGASA call fails. This tool sets no data_status field on a success or an unknown-location result. Check data_source and caveats instead. Examples:

get_weather_forecast("Manila") 3-day default forecast get_weather_forecast("Cebu City", days=2) 2-day forecast get_weather_forecast("Wakanda") unknown location, no coordinates found

On failure, a location with no known coordinates returns days: [] and a caveat, with no data_status or upstream_error key. An Open-Meteo fetch failure, or a PSGC outage during location resolution, returns data_status "unavailable", upstream_error: true, days: [], and the real error in caveats. An Open-Meteo response with no daily forecast entries returns data_status "indeterminate" instead, and is never cached.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoForecast days (1-10, default 3).
locationYesMunicipality, city, or province name.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.4.0
  2. Removedv1.0.4
  3. Addedv1.0.2

TDQS

A4.6/5.0
Behavior5/5

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

The annotations already indicate readOnly, idempotent, and non-destructive behavior. The description goes beyond by detailing the data source fallback logic, the exact data_status values on failures (e.g., 'unavailable', 'indeterminate'), and caching behavior. This provides a rich picture of expected behavior without contradicting 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 well-structured: a clear opening statement, concise paragraphs on fallback and error behavior, and illustrative examples. It delivers necessary details without redundancy or fluff, making it easy to parse.

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?

Given the complexity of data sources and error handling, the description covers key output fields (data_status, caveats, upstream_error, days) and explains when they appear. Although the full output schema is not shown, the description provides sufficient context for an agent to interpret results correctly.

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 both parameters with descriptions (location as municipality/city/province, days as integer with default 3). The description adds practical examples for parameter usage and clarifies error outputs, which enhances understanding. Since schema coverage is 100%, the baseline is 3, but the examples push it to 4.

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 the tool's function: 'Get the weather forecast for a Philippine location.' It uses a specific verb and resource, and the scope is well-defined. It also distinguishes itself from weather-related siblings like get_weather_alerts and get_active_typhoons by focusing on forecasts.

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 explains the fallback behavior (PAGASA vs Open-Meteo) and how to interpret error cases via data_source and caveats, which guides usage. However, it does not explicitly mention when to use this tool over alternatives like get_weather_alerts, so it falls short of fully explicit guidance.

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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