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BACH-AI-Tools

Weatherapi Com MCP Server

realtime_weather_api

Get current weather data for any location using coordinates, city names, zip codes, or IP addresses. Returns real-time conditions in JSON or XML format.

Instructions

Current weather or realtime weather API method allows a user to get up to date current weather information in json and xml. The data is returned as a Current Object.

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
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 output format (json/xml) and that data is returned as a 'Current Object', but lacks critical details: authentication requirements, rate limits, error handling, whether it's read-only (implied but not stated), or any performance characteristics. The description is insufficient for a tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

The description is reasonably concise (two sentences) but has structural issues. The first sentence is somewhat redundant ('Current weather or realtime weather API method'). The second sentence adds value by specifying the return object type. However, it could be more front-loaded with clearer differentiation from siblings.

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?

Given no annotations, no output schema, and a single parameter with full schema coverage, the description is incomplete. It doesn't explain what a 'Current Object' contains, doesn't address authentication or rate limits, and provides minimal behavioral context. For a weather API tool among many siblings, more completeness is needed to guide proper usage.

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 fully documents the single parameter 'q' with extensive examples. The description adds no parameter information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in description.

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: 'get up to date current weather information' with specific output formats (json and xml). It distinguishes from siblings like forecast_weather_api and history_weather_api by focusing on current/realtime data. However, it doesn't explicitly contrast with all siblings (e.g., alerts_api, marine_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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over forecast_weather_api for future predictions, history_weather_api for past data, or other siblings like marine_weather_api for specialized data. Usage context is implied but not stated.

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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