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BACH-AI-Tools

Weatherapi Com MCP Server

future_weather_api

Retrieve 3-hour interval weather forecasts for dates 14 to 300 days ahead using location coordinates, city names, or postal codes.

Instructions

Future weather API method returns weather in a 3 hourly interval in future for a date between 14 days and 300 days from today in the future.

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
langNoReturns 'condition:text' field in API in the desired language
dtYes'dt' should be between 14 days and 300 days from today in the future in yyyy-MM-dd format (i.e. dt=2023-01-01)
Behavior2/5

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

With no annotations provided, the description carries full burden but provides minimal behavioral information. It mentions the temporal constraints (14-300 days future, 3-hour intervals) but doesn't cover rate limits, authentication requirements, error conditions, response format, or what happens with invalid parameters. For an API tool with no annotations, this leaves significant gaps.

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 a single, reasonably concise sentence that communicates the core functionality. However, it could be slightly more structured by separating the temporal constraints from the data format, and it doesn't front-load the most critical information about what distinguishes this from other weather tools.

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 an API tool with no annotations and no output schema, the description is insufficient. It doesn't explain what weather data is returned (temperature, precipitation, etc.), the response format, error handling, or authentication requirements. Given the complexity of weather data and lack of structured metadata, the description should provide more complete context for effective tool use.

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 fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline score of 3 reflects adequate coverage through the schema alone.

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 returns weather data in 3-hour intervals for future dates between 14 and 300 days from today. It specifies the verb ('returns'), resource ('weather'), and temporal scope ('future'), but doesn't explicitly differentiate from sibling tools like forecast_weather_api or history_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 about when to use this tool versus alternatives like forecast_weather_api (likely for shorter-term forecasts) or history_weather_api. The description only states what the tool does, not when it should be selected over similar weather-related tools.

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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