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BACH-AI-Tools

Weather API167 MCP Server

forecast_weather

Retrieve forecast weather data by specifying coordinates, city name, or zip code. Customize output with number of timestamps, units, and language for precise weather predictions.

Instructions

Fetch forecast weather data using coordinate value, place name or zip code

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lonNoLongitude coordinate(Note. lat and lon value has priority over both place and zip value). use the lat and lon for more accurate and reliable weather data0
latNoLatitude coordinate(Note. lat and lon value has priority over both place and zip value). use the lat and lon for more accurate and reliable weather data0
placeNoCity name, state code (only for the US) and country code divided by comma like Zion,VA,US . Please use ISO 3166 country codes. Note. place value has priority over zip value
zipNoZip code with optional country code(Note. Zip value will only be used if both lat,lon and place name not provided)
cntNoNumber of timestamps to retrieve (up to a maximum allowed by the API)
unitsNoUnit type (standard, metric, and imperial)
typeNoForcast Type
modeNoMode type
langNoLanguage type
Behavior2/5

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

No annotations exist, so the description must disclose behaviors. It only says 'fetch', implying a read operation, but omits details like auth needs, rate limits, data freshness, or that coordinates have priority. Minimal transparency.

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 sentence of 13 words – concise and front-loaded with the core purpose. No extraneous wording, but it could be slightly more structured.

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?

With 9 parameters, no required fields, and no output schema, the description is insufficient. It doesn't explain return format, cnt limit, units meanings, or what type/mode/lang do. Users would lack essential context.

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 coverage is 100%, but the description adds value by explaining priority rules (lat/lon over place over zip) and that lat/lon are more accurate. This complements the schema descriptions, though other parameters like type, mode, lang lack additional context.

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 states the tool fetches forecast weather data using coordinates, place name, or zip code. It clearly identifies the resource (forecast weather) and how to specify location, though it doesn't explicitly differentiate from sibling tools like current_weather.

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 like current_weather or earthquake_data. There are no scenarios or prerequisites mentioned.

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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