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Get Subsea Cables

get_subsea_cables
Read-onlyIdempotent

Subsea (submarine) cable landings near a coordinate, or the global cable catalogue. The physical internet crossing an ocean lands at a finite number of points, and distance to one is a real siting factor for anything latency- or transit-sensitive. Pass lat+lon (+radius_km) for LANDING POINTS near a site — each with name, coordinates and distance_km. Omit coordinates for the CATALOGUE of tracked cables (712 tracked; each with cable_id, name, owners, length_km, rfs_year, is_planned — sparse fields are null, not guessed). ★ READ field_coverage AND connectivity_note BEFORE DRAWING A CONCLUSION: cable_count per landing point is NOT populated — the ingest writes the column but the upstream TeleGeography feed does not supply what it derives from, so every row carries the default 0. That is why connectivity_grade comes back null rather than graded: proximity to a landing point does NOT establish how many cables are reachable from it, and DC Hub will not infer a grade it cannot source. A filter over cable_count returns nothing for the same reason. Treat 0 as UNKNOWN, never as "no cables". Answers "which subsea cables land near this Virginia site" and "how far is the nearest cable landing from my campus". Try: get_subsea_cables lat=36.85 lon=-75.98 radius_km=200 — or get_subsea_cables (no args) for the catalogue. Do NOT use for terrestrial fiber routes (get_fiber_intel), a parcel fiber verdict (get_fiber_readiness), metro fiber depth (get_metro_fiber), or internet-exchange / peering density (get_peering_intel).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude of the site, e.g. 36.85 — with lon, returns landing points near it; omit both for the cable catalogue
lngNoAlias for lon — either name works
lonNoLongitude of the site, e.g. -75.98
limitNoMax results to return (1-500; default varies by tool)
latitudeNoAlias for lat — either name works
longitudeNoAlias for lon — either name works
radius_kmNoSearch radius in km around lat/lon for landing points (default 200)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
quotaNoCaller quota state (remaining calls, tier).
_entityNoPayload class (facility|market|iso_grid|queue_results|deal|report|response); branch on it.
citationNoHow to attribute DC Hub: {source, url, license, cite_as, retrieved_at}, or the attribution line as a string.
provenanceNoSource, method, as_of, verification_counts, license, cite_as; quote it when citing.
_front_doorNoFirst-tool hint: plan_query(intent) returns the ordered plan.
_return_loopNoNext-session delta call (get_changes).
site_evaluation_handoffNoFollow-up calls [{tool, parameters, why}] when the payload carries coordinates.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added
  2. Removed
  3. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), yet the description adds substantial non-obvious behavioral context: cable_count is written as a default 0 by ingest, connectivity_grade returns null, filtering on cable_count yields nothing, and 0 must be read as UNKNOWN. This is exactly the kind of data-provenance caveat an agent cannot infer from structured fields.

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

Conciseness4/5

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

Purpose and mode are front-loaded, followed by the critical caveat, example calls, and routing exclusions. It is dense and somewhat repetitive (the cable_count/0 point is restated), but each block is load-bearing for a tool with a genuine data-quality trap.

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?

With an output schema present, the description needn't explain return shape, and it still supplies the unavailable-field caveat, both operating modes, defaults, worked examples, and alternative-tool routing. An agent has everything required to call it correctly and interpret the results.

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 the schema already documents lat/lon aliases, the radius_km default of 200, and that omitting coordinates returns the catalogue. The description largely restates this mode behavior rather than adding syntax or format detail beyond the schema, so the baseline 3 applies.

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 states a specific verb and resource (subsea/submarine cable landings + global cable catalogue) and explicitly distinguishes its two operating modes: coordinate-based landing-point lookup vs. no-arg catalogue. It is immediately separable from sibling fiber tools like get_fiber_intel and get_metro_fiber.

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?

It gives explicit when-to-use conditions (pass lat+lon+radius_km for landing points, omit coordinates for the catalogue), concrete example calls, and a hard 'Do NOT use for' list naming four alternatives (get_fiber_intel, get_fiber_readiness, get_metro_fiber, get_peering_intel). Nothing about tool selection 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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