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mysukriramli

disaster-wire

by mysukriramli
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

find_disasters_near_coordinates

Read-onlyIdempotent

Locate active natural disasters within a chosen radius of target coordinates to assess geographic hazard exposure for facilities, cities, ports, or data centers.

Instructions

Locate active natural disasters within a radial distance (km) of target latitude and longitude coordinates. Use when evaluating geographic hazard exposure for specific facilities, cities, ports, or data centers at known coordinates. Do not use for country-wide or categorical queries (use list_active_disasters instead) or if you already hold an event_id (use get_disaster_by_id instead). Calculates great-circle distance via Haversine formula. Results are sorted ascending by distance_from_query_km. Returns {'matched_count': 0, 'results': []} if no incidents fall within radius.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latitudeYesTarget latitude in decimal degrees (-90.0 to 90.0, WGS-84 standard).
longitudeYesTarget longitude in decimal degrees (-180.0 to 180.0, WGS-84 standard).
radius_kmNoRadial search perimeter measured in kilometers (km) from target coordinates (1.0 to 5000.0, defaults to 500.0 km).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed15 schema fields changedv0.1.4
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / latitude / description
      Added value: +"Target latitude in decimal degrees (-90.0 to 90.0, WGS-84 standard)."
    • addedInput schema / properties / latitude / maximum
      Added value: +90
    • addedInput schema / properties / latitude / minimum
      Added value: +-90
    • removedInput schema / properties / latitude / title
      Removed value: -"Latitude"
    • addedInput schema / properties / longitude / description
      Added value: +"Target longitude in decimal degrees (-180.0 to 180.0, WGS-84 standard)."
    • addedInput schema / properties / longitude / maximum
      Added value: +180
    • addedInput schema / properties / longitude / minimum
      Added value: +-180
    • removedInput schema / properties / longitude / title
      Removed value: -"Longitude"
    • addedInput schema / properties / radius_km / description
      Added value: +"Radial search perimeter measured in kilometers (km) from target coordinates (1.0 to 5000.0, defaults to 500.0 km)."
    • addedInput schema / properties / radius_km / maximum
      Added value: +5000
    • addedInput schema / properties / radius_km / minimum
      Added value: +1
    • removedInput schema / properties / radius_km / title
      Removed value: -"Radius Km"
    • removedInput schema / title
      Removed value: -"find_disasters_near_coordinatesArguments"
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "result": {
-      "title": "Result",
-      "type": "string"
-    }
-  },
-  "required": [
-    "result"
-  ],
-  "title": "find_disasters_near_coordinatesOutput",
-  "type": "object"
-}New value: +null
  2. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare the safe/read-only/idempotent profile, so the description's added value is the algorithm (Haversine great-circle), result ordering (ascending distance_from_query_km), and the explicit empty-result shape. It stops short of noting pagination or result caps, but the behavioral picture is strong for a bounded read-only query.

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?

Four tight sentences: purpose first, then usage conditions, then algorithm, then output ordering and empty-case. No filler, and the most decision-relevant information (when to use / when not to) is front-loaded.

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?

For a 3-parameter, read-only spatial query with no output schema, the description covers purpose, routing, computation method, sort order, and the zero-match response contract. An agent has everything needed to call it and interpret the result.

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% and each parameter already documents units, ranges, and defaults. The description reinforces unit (km) and the radial nature of radius_km but adds no syntax or semantics beyond the schema, which is the expected baseline when the schema does the heavy lifting.

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?

States a specific verb (Locate) plus resource (active natural disasters) and scope (radial distance in km from coordinates). An agent can distinguish it from list_active_disasters and get_disaster_by_id purely from the text, without opening any schema.

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

Usage Guidelines5/5

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

Explicitly gives the use case (hazard exposure for facilities, cities, ports, data centers at known coordinates) and two named exclusions with the alternative tool to use instead in each case (country-wide/categorical, or known event_id). Nothing is left to inference.

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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