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smithery-ai-national-weather-service

get_weather_alerts

Get active weather alerts, warnings, watches, and advisories for locations in the United States. Perfect for 'Are there any weather alerts in [US location]?' questions. Covers severe weather, winter storms, heat warnings, flood alerts, and more.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locationYesUS location as coordinates (lat,lng) in decimal degrees OR 2-letter state/territory code. Examples: '40.7128,-74.0060' for New York City, 'CA' for California, 'PR' for Puerto Rico. Valid state codes: AL, AK, AS, AR, AZ, CA, CO, CT, DE, DC, FL, GA, GU, HI, ID, IL, IN, IA, KS, KY, LA, ME, MD, MA, MI, MN, MS, MO, MT, NE, NV, NH, NJ, NM, NY, NC, ND, OH, OK, OR, PA, PR, RI, SC, SD, TN, TX, UT, VT, VI, VA, WA, WV, WI, WY, MP, PW, FM, MH.
severityNoFilter by alert severity: 'extreme' (life-threatening), 'severe' (significant threat), 'moderate' (possible threat), 'minor' (minimal threat), or 'all' (default - shows all active alerts).all

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

No annotations provided, so description carries full disclosure burden. Adds valuable context about covered alert types ('severe weather, winter storms, heat warnings, flood alerts'), but omits operational details like real-time latency, behavior when no alerts exist, or data source freshness that would be necessary for a mutation tool.

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?

Three sentences with zero waste: sentence 1 states purpose, sentence 2 provides usage context, sentence 3 specifies coverage scope. Front-loaded with the core action. Every sentence earns its place with no redundancy.

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 simple 2-parameter schema with 100% coverage and no output schema, description adequately covers geographic constraints (US-only), use case patterns, and alert categories. Could mention the severity filtering capability, though this is well-handled by the schema.

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 coverage is 100% with both parameters fully documented (location includes format examples and valid codes; severity includes enum descriptions). Description implies the location parameter through '[US location]' reference but adds no semantic detail beyond what the schema already provides. Baseline 3 appropriate for high-coverage schemas.

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?

Description opens with specific verb 'Get' and clearly identifies resource (active weather alerts, warnings, watches, and advisories) and scope (United States). Effectively distinguishes from sibling forecast tools like get_weather_forecast and get_current_weather by focusing specifically on alerts.

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?

Provides clear positive usage example: 'Perfect for Are there any weather alerts in [US location]? questions.' This gives the agent a strong trigger pattern. However, lacks explicit contrast with alternatives (e.g., when to use get_current_weather vs this tool) or negative constraints.

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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