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DC Hub — Data Center & Energy Intelligence

Get Metro Fiber

get_metro_fiber
Read-onlyIdempotent

Compare fiber depth across US data-center metros: get density scores, carrier counts, route-miles, on-net buildings, tier, IX points, and carrier-by-carrier dark-fiber breakdowns.

Instructions

Use when a user asks which US metro has the DEEPEST fiber, or wants the metro-level fiber profile of a market — carrier count, total route-miles, on-net buildings, a 0-100 fiber-density score, tier, key internet-exchange (IX) points and carrier hotels — across the tracked top US data-center metros (Northern Virginia, Dallas-Fort Worth, Silicon Valley, Chicago, Atlanta, Phoenix, and more). Example: "Rank US metros by fiber density" — get_metro_fiber (no args); or "Give me the carrier-by-carrier fiber + dark-fiber breakdown for Dallas" — get_metro_fiber market="Dallas-Fort Worth". Params: market (optional metro name OR slug, e.g. "Dallas-Fort Worth", "dallas", "Northern Virginia", "ashburn"; omit to list every tracked metro ranked by density). Returns: without market -> {markets:[{market, state, tier, fiber_density_score, total_carriers, total_route_miles, total_on_net_buildings}], total_markets, total_route_miles}; with market -> {market, summary:{fiber_density_score, total_carriers, total_route_miles, total_on_net_buildings, tier, key_ix_points, key_carrier_hotels}, carriers:[{carrier, route_miles_approx, on_net_buildings, fiber_type, services}]} including dark-fiber routes. Cite DC Hub (dchub.cloud, CC-BY-4.0). Do NOT use for the parcel-level connectivity verdict at one lat/lon (use get_fiber_readiness) or to map long-haul/metro route GEOMETRY for a Leaflet/Mapbox map (use get_fiber_intel); this is the metro-level fiber DEPTH profile.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketNoOptional metro name or slug for a single-market deep dive (carrier-by-carrier + dark fiber), e.g. "Dallas-Fort Worth", "dallas", "Northern Virginia", "ashburn". Omit to list every tracked metro ranked by fiber density.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
quotaNoCaller quota state (remaining calls, tier) when available.
_entityNoPayload class discriminator (e.g. facility|market|iso_grid|queue_results|deal|report|response) — branch on this before parsing the rest.
citationNoMachine-readable citation: how to attribute DC Hub (dchub.cloud) for this payload. Normally an OBJECT {source, url, license, cite_as, retrieved_at}; a bare string is accepted and carries the attribution line itself.
provenanceNoCollection-level provenance block: {source, method, as_of, verification_counts, cite_url_template, license, cite_as}. Quote the verification level when citing.
_front_doorNoIn-band front-door hint (first workflow-entry tool of a session): call plan_query(intent) first for the ordered multi-step plan.
_return_loopNoSuggested next-session delta call (get_changes since=24h) so you pull only what changed.
site_evaluation_handoffNoPre-built follow-up calls (analyze_site / get_water_risk args) when the payload carries coordinates — an array of {tool, parameters, why} entries.
Install Server

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds meaningful behavior beyond annotations: it explains the two distinct output shapes depending on whether the market param is supplied, includes dark-fiber coverage, and includes a citation requirement ('Cite DC Hub'). The only minor gap is not stating rate limits or freshness, but that is not required given the strong annotation coverage.

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?

The description is front-loaded with the key use case, includes concrete examples, and packs in essential return-format details. It is dense but every sentence earns its place. It could be slightly shortened by trimming the detailed return schema (since an output schema exists), but the operational guidance justifies the length.

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?

Given the tool's modest complexity (one optional parameter, no required params, output schema present, annotations covering read-only/idempotent), the description is complete. It covers when to use, what it returns in both modes, the data source/citation, and when NOT to use it. An agent has everything needed to select and invoke it correctly.

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?

Schema coverage is 100% and the schema already documents the market parameter thoroughly. The description adds value by giving concrete examples ('Dallas-Fort Worth', 'dallas', 'ashburn'), clarifying slug vs name, and explaining the behavioral difference between omitting market (ranked list) vs providing it (deep dive). This goes beyond what the schema alone provides.

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 is highly specific: it names the verb 'get', the resource 'metro fiber profile', and enumerates exact fields (carrier count, route-miles, on-net buildings, density score, tier, IX points, carrier hotels). It also distinguishes itself from sibling tools by explaining what it is NOT for (parcel-level verdicts, geometry mapping). An agent can clearly understand what this tool does and how it differs from get_fiber_readiness and get_fiber_intel.

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?

The description explicitly provides when-to-use guidance: 'Use when a user asks which US metro has the DEEPEST fiber, or wants the metro-level fiber profile of a market.' It also names the two sibling alternatives and the conditions that select them: do NOT use for parcel-level latency/connectivity (use get_fiber_readiness) or route geometry for maps (use get_fiber_intel). This is exemplary routing guidance.

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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