Skip to main content
Glama
BACH-AI-Tools

Weatherapi Com MCP Server

forecast_weather_api

Get up to 14-day weather forecasts with astronomy data, daily predictions, and hourly intervals for any location to plan activities and prepare for conditions.

Instructions

Forecast weather API method returns upto next 14 day weather forecast and weather alert as json. It contains astronomy data, day weather forecast and hourly interval weather information for a given city.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesQuery parameter based on which data is sent back. It could be following: Latitude and Longitude (Decimal degree) e.g: q=48.8567,2.3508 city name e.g.: q=Paris US zip e.g.: q=10001 UK postcode e.g: q=SW1 Canada postal code e.g: q=G2J metar: e.g: q=metar:EGLL iata:<3 digit airport code> e.g: q=iata:DXB auto:ip IP lookup e.g: q=auto:ip IP address (IPv4 and IPv6 supported) e.g: q=100.0.0.1
daysNoNumber of days of forecast required.3
langNoReturns 'condition:text' field in API in the desired language
dtNoIf passing 'dt', it should be between today and next 10 day in yyyy-MM-dd format.
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the 14-day forecast limit and JSON format, but lacks critical information about rate limits, authentication requirements, error conditions, or whether this is a read-only operation. The description doesn't adequately compensate for the absence of 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 appropriately concise with two sentences that efficiently convey the core functionality. It's front-loaded with the main purpose and includes key constraints (14-day limit, JSON format). While effective, it could be slightly more structured with clearer separation of different forecast components.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 4-parameter tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the return structure looks like (beyond 'json'), doesn't mention error handling, and provides no context about the sibling tools. The absence of output schema means the description should compensate by explaining return values, which it doesn't do.

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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it mentions 'for a given city' which aligns with the 'q' parameter but provides no additional context about parameter interactions or usage patterns.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: to return weather forecasts up to 14 days with astronomy data, daily forecasts, and hourly information for a given city. It specifies the verb ('returns') and resource ('weather forecast'), but doesn't explicitly differentiate from siblings like 'future_weather_api' or 'realtime_weather_api'.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. With multiple weather-related sibling tools (future_weather_api, realtime_weather_api, etc.), the description offers no context about appropriate use cases, prerequisites, or distinctions from similar tools.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/BACH-AI-Tools/weatherapi_com'

If you have feedback or need assistance with the MCP directory API, please join our Discord server