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CorporateTravelDC

CorporateTravel Dispatch MCP

Official
by CorporateTravelDC
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

dispatch_get_weather

Read-onlyIdempotent

Retrieve current weather conditions for DC-area airports, including ceiling, visibility, wind, temperature, and flight category.

Instructions

Get the current METAR weather snapshot for DC-area airports.

Returns parsed METARs from AviationWeather.gov ADDS for stations in and around the DC area (KIAD, KDCA, KBWI, KJYO, KHEF, KCGS, etc.). Data includes ceiling, visibility, wind, temperature, altimeter.

Returns: str: JSON dict with 'stations' list. Each station entry: icao (str), obs_time (str), wind_dir (int|null), wind_speed_kt (int), wind_gust_kt (int|null), visibility_sm (float), ceiling_ft (int|null), temp_c (float), dewpoint_c (float), altimeter_inhg (float), flight_category (str: VFR/MVFR/IFR/LIFR), raw_metar (str).

Examples: - "What's the ceiling at Dulles?" -> call, find KIAD entry, check ceiling_ft - "Is it VFR at DC area airports?" -> call, check flight_category per station

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.3/5.0
Behavior4/5

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

Adds behavioral context beyond annotations: describes return format, data source (AviationWeather.gov), and example queries; annotations already cover idempotency and read-only nature.

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?

Concise, front-loaded with purpose, followed by structured return fields and usage examples; every sentence adds value.

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

Completeness5/5

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

Completely covers the tool's behavior for a no-param function with rich output; details return format and example use cases, matching the complexity.

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?

No parameters exist, so baseline 4 applies; description adds value by detailing output fields and examples, compensating for lack of input parameters.

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?

Clearly states it retrieves METAR weather snapshot for DC-area airports, specifying the data source and location, distinguishing it from sibling tools that handle alerts, briefs, etc.

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

Usage Guidelines3/5

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

Purpose is implied but no explicit guidance on when to use vs alternatives; context suggests weather-related queries but lacks exclusions or when-not-to-use.

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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