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DC Hub — Data Center & Power Intelligence

by cloud.dchub

Get Fiber Readiness

get_fiber_readiness
Read-onlyIdempotent

Use when you need the FIBER-READINESS / connectivity verdict for ONE parcel or site (lat/lon): near-net distance to a carrier-served facility, how many distinct fiber carriers are reachable, and whether there is single-carrier risk (no path diversity). This is the parcel connectivity answer engineering site-selectors screen on. Example: "Is this Loudoun County parcel fiber-ready and how many carriers can serve it?" — get_fiber_readiness lat=39.04 lon=-77.48 radius_km=50. Params: lat (-90..90, required), lon (-180..180, required), radius_km (search radius in km, default 50, range 5-200). Returns: {score 0-100 (null when not scored — see carrier_data_coverage), near_net_bucket ("on-net"|"near-net"|"acceptable"|"build-required"|"unknown"), nearest_carrier_km, carrier_count, top_carriers:[{carrier, distance_km}], single_carrier_risk (bool, null when not scored), fiber_coverage_km, verdict_short, carrier_data_coverage ("confirmed"|"none_in_region")}. IMPORTANT — "unknown" is NOT "bad": carrier presence comes from PeeringDB, which is global but thin outside dense US/EU metros, so DC Hub distinguishes "no carrier serves this point" from "PeeringDB does not describe this region". When carrier_data_coverage is "none_in_region" the bucket is "unknown", score/single_carrier_risk are null, and NOTHING about the site fiber has been measured — do not report it as greenfield, unserved, or a build-required site. Only carrier_data_coverage "confirmed" with carrier_count 0 means a fiber build is genuinely required. Do NOT use to map carrier ROUTES between metros (use get_fiber_intel) or for a full multi-factor site suitability score (use analyze_site).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoSite latitude in decimal degrees (-90 to 90, required), e.g. 39.04
lngNoAlias for lon — either name works
lonNoSite longitude in decimal degrees (-180 to 180, required), e.g. -77.48
latitudeNoAlias for lat — either name works
longitudeNoAlias for lon — either name works
radius_kmNoSearch radius in km for reachable fiber carriers (default 50, range 5-200)

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.

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds the crucial behavioral nuance that an 'unknown' near_net_bucket is NOT 'bad' — explaining the PeeringDB thin-coverage caveat, that carrier_data_coverage='none_in_region' yields null score/risk and 'unknown' bucket and must not be reported as greenfield/unserved/build-required, and that only 'confirmed' with carrier_count 0 means a real fiber build. This is precisely the kind of interpretation risk that an agent cannot infer from schema or annotations, and it's disclosed clearly.

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 long, but every sentence earns its place: the usage trigger, the example with params, the return-field map, the PeeringDB caveat, and the exclusions. The most decision-relevant content (when to use and the 'unknown is not bad' caveat) is front-loaded. It loses one point only because the parameter list and return-field list partially duplicate the input schema and output schema, which are already structured, so some text is redundant.

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 single-parcel verdict tool with a rich output schema that already documents the return shape, this is complete. It explains the one ambiguity an agent will hit (coverage-driven 'unknown' buckets), gives the exclusions, names the alternatives, and the output schema covers return values. Nothing an agent needs to invoke it correctly is missing.

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 every parameter (lat, lon, lng, latitude, longitude, radius_km) is described inline with ranges and defaults. The description adds the example values (lat=39.04, lon=-77.48, radius_km=50) and clarifies the aliases, but the main added value is the return-object semantics rather than parameter syntax. Baseline 3 applies because the schema is complete; the example and the alias note nudge it to 4.

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+resource ('get... FIBER-READINESS / connectivity verdict for ONE parcel or site') and a precise scope, and includes a concrete example. It differentiates itself from the two obvious siblings by naming get_fiber_intel and analyze_site, so an agent can tell them apart without opening either 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?

The description opens with 'Use when you need...', scopes it explicitly to one parcel or site vs. routes/metro context (get_fiber_intel) vs. multi-factor score (analyze_site), and closes with explicit do-not-use exclusions naming the alternatives. It also gives a worked example invocation. There is no ambiguity about when to select this tool.

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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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes despite some thematic overlap, and each description includes explicit 'Do NOT use' guidance to prevent misselection. However, a few pairs like search_intelligence vs semantic_search are nearly identical in function, and the sheer number of tools increases the chance of selecting the wrong one without careful reading.

Naming Consistency4/5

The vast majority of tools follow a predictable 'get_*' prefix for data reads, and many others use verb_noun patterns (analyze_*, rank_*, save_*, set_*). There are a handful of outliers like ai_capacity_index, grid_transition_radar, and site_selection_canvas that break the pattern, but overall the conventions are consistent enough for an agent to infer meaning.

Tool Count2/5

With 82 tools, this server is extremely heavy compared to typical MCP servers (3-15 tools). While the domain is broad, many tools serve narrow sub-purposes and could be consolidated (e.g., multiple site-scoring variants, multiple grid telemetry endpoints). The count overwhelms an agent's ability to choose efficiently and feels like over-fragmentation rather than necessary granularity.

Completeness4/5

The tool surface covers the full lifecycle of data-center siting intelligence: site analysis, grid, fiber, water, climate, tax, permitting, deals, news, saved-site management, and meta-planning. Minor gaps exist (e.g., no delete or update operations for saved sites), but the core workflows are well-supported and the descriptions are comprehensive.

Resources