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RJW34

Weather Edge MCP Server

list_cities

Retrieve supported cities, settlement stations, and calibration parameters for weather prediction market analysis.

Instructions

List supported cities, settlement stations, and calibration parameters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler for 'list_cities'. Decorated with @mcp.tool() and returns format_city_list() result.
    @mcp.tool()
    def list_cities() -> str:
        """List supported cities, settlement stations, and calibration parameters."""
        return format_city_list()
  • format_city_list() helper that builds the formatted string of supported cities, their stations, sigma, and bias.
    def format_city_list() -> str:
        lines = ["# Supported Cities", ""]
        for cfg in CITIES.values():
            lines.append(
                f"- {cfg.key}: {cfg.label} | station={cfg.station} | metar={cfg.metar_station} | sigma={cfg.sigma} | bias={cfg.forecast_bias:+.1f}"
            )
        return "\n".join(lines)
  • CITIES dict defining the supported cities and their config (label, station, metar, sigma, bias, etc.) that list_cities reports.
    CITIES: dict[str, CityConfig] = {
        "nyc": CityConfig("nyc", "New York City", "Central Park", "KNYC", "OKX", 33, 37, "KXHIGHNY", 3.0, -1.0),
        "chicago": CityConfig("chicago", "Chicago", "Midway", "KMDW", "LOT", 76, 73, "KXHIGHCHI", 3.0, -0.5),
        "denver": CityConfig("denver", "Denver", "Denver", "KDEN", "BOU", 62, 60, "KXHIGHDEN", 4.0, 0.0),
        "miami": CityConfig("miami", "Miami", "MIA Airport", "KMIA", "MFL", 75, 54, "KXHIGHMIA", 3.5, -3.0),
        "la": CityConfig("la", "Los Angeles", "Los Angeles Downtown", "KLAX", "LOX", 154, 44, "HIGHLA", 3.5, 0.0),
    }
  • Registration via @mcp.tool() decorator on line 81; line 36 shows the same pattern for context.
    @mcp.tool()
Behavior2/5

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

With no annotations, the description is the sole source of behavioral information. It only describes the action as 'List', implying read-only, but offers no details on side effects, data source, or reliability. More context is needed for safe invocation.

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?

The description is a single sentence of eight words, front-loaded with the verb. Every word is essential, and there is no redundancy or filler.

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

Completeness3/5

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

The tool has no parameters and an output schema exists, so the description is minimally adequate. However, it does not hint at the structure or size of the output, nor any filtering capabilities. For a trivial tool, this is sufficient but not thorough.

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?

The input schema has zero parameters, so schema description coverage is 100%. The description does not need to explain parameters. Baseline 4 is appropriate as no additional semantic is required.

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?

The description clearly states the tool lists supported cities, settlement stations, and calibration parameters. The verb 'List' combined with specific resources differentiates it from sibling tools that deal with signals, forecasts, and observations.

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. There is no mention of prerequisites, context, or conditions for invocation.

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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