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DC Hub — Data Center & Power Intelligence

by cloud.dchub

Nearby Infrastructure

get_infrastructure
Read-onlyIdempotent

Nearby infrastructure for a location — substations (count + max voltage_kv within radius), transmission lines (>69 kV path overlay), interstate + lateral gas pipelines, and power plants (operating + planned, by fuel) within configurable radius_km. Returns distance + capacity for each, joined to HIFLD/EIA. Answers "what is near this parcel", "how far is the nearest substation and what voltage is it". Try: get_infrastructure lat=33.45 lon=-112.07 radius_km=25. Returns raw nearby assets; do NOT use for a single scored site-suitability verdict (use analyze_site).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoCenter latitude in decimal degrees (-90 to 90, required), e.g. 33.45
lngNoAlias for lon — either name works
lonNoCenter longitude in decimal degrees (-180 to 180, required), e.g. -112.07
layerNoOptional single asset layer to return, e.g. substations, transmission, pipelines, power_plants
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 kilometers around the point, e.g. 25
min_voltage_kvNoOnly include transmission/substations at or above this voltage in kV, e.g. 69

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

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, so the safety profile is covered. The description adds behavioral context beyond annotations: it returns raw assets joined to HIFLD/EIA, includes counts and max voltage for substations, and notes the configurable radius. It does not mention pagination or response format, but the output schema exists, so this is acceptable.

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 dense paragraph that front-loads the core purpose, enumerates asset types, gives concrete usage examples, and routes to an alternative—all without wasted words. Every sentence adds information; no filler.

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 complexity (multiple asset layers, filters, aliases) and the presence of a full input schema and output schema, the description covers everything an agent needs: what assets are returned, how radius works, example invocation, and the explicit exclusion of scored verdicts. Nothing critical 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 description coverage is 100%, so each parameter is already documented. The description adds meaningful context beyond the schema: it explains that radius_km is the configurable search radius, ties min_voltage_kv to transmission/substations (>69 kV overlay), and provides a concrete example of lat/lon/radius usage. This adds value beyond the structured schema.

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 and resource: 'get_infrastructure' returns nearby infrastructure assets for a location, enumerating exact asset types (substations, transmission lines, pipelines, power plants) and the kind of data returned (distance, capacity). It also names the sibling 'analyze_site' and contrasts itself by being raw vs. scored, making the purpose unambiguous and distinguishable.

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 answers representative questions ('what is near this parcel', 'how far is the nearest substation and what voltage is it'), gives a concrete example call (lat=33.45 lon=-112.07 radius_km=25), and explicitly states when NOT to use it ('do NOT use for a single scored site-suitability verdict (use analyze_site)'). This is clear when-to-use vs. alternative 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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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