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DC Hub — Data Center & Power Intelligence

by cloud.dchub

Generate Site Analysis

generate_site_analysis
Read-onlyIdempotent

Use when a user wants a SHAREABLE, branded multi-page Site Analysis PDF for ONE lat/lon (a powered-land parcel, a candidate campus) — the polished client deliverable, not just a score. Example: "Make the Site Analysis PDF for this Carrier Mills parcel, 150 MW, for TON Infrastructure." — generate_site_analysis lat=37.694 lon=-88.65 capacity_mw=150 prepared_for="TON Infrastructure" prepared_by="Martone Advisors". Params: lat (-90 to 90, required), lon (-180 to 180, required), capacity_mw (target load MW, e.g. 50-500), prepared_for (client name on the cover), prepared_by (your firm — brands the report; defaults to DC Hub), latency_target (optional metro override; default = nearest real carrier hotel). Returns: {survey:{verdict, power/transmission, gas, water, air-permitting, fiber carriers, latency-to-nearest-carrier-hotel, market, tax}, pdf_report_url}. pdf_report_url is a ready-to-open link to download the branded 5-page PDF — no login needed, valid ~7 days; hand it to your human. For just the numeric suitability score (no PDF), use analyze_site instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoSite latitude in decimal degrees (-90 to 90, required), e.g. 37.694
lngNoAlias for lon — either name works
lonNoSite longitude in decimal degrees (-180 to 180, required), e.g. -88.65
latitudeNoAlias for lat — either name works
use_caseNoOptional workload descriptor to tailor the report, e.g. "AI training campus"
longitudeNoAlias for lon — either name works
capacity_mwNoTarget power load for the build in megawatts (MW), e.g. 150 (typical 50-500)
prepared_byNoYour firm name that brands the report; defaults to DC Hub, e.g. "Martone Advisors"
prepared_forNoClient name printed on the report cover, e.g. "TON Infrastructure"
latency_targetNoOptional metro to measure latency against; default = nearest real carrier hotel

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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond those hints: the result is a branded 5-page PDF with a ready-to-open, no-login link valid ~7 days, plus defaults for prepared_by and latency_target. No contradiction with annotations.

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?

Though longer than average, every sentence carries selection or invocation information: trigger condition, example call, parameter meanings/defaults, return shape, link expiration, and sibling routing. It is front-loaded with the use condition and closes with the alternative tool.

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 10-parameter PDF-generation tool, the description covers the full call path: when to use it, how to construct arguments, what the return envelope contains, and how to handle the result (hand the link to a human). The existence of an output schema lowers the burden for return-value documentation, and the description still summarizes the survey object.

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%, so the baseline is 3. The description adds meaning beyond the schema by spelling out defaults ('prepared_by ... defaults to DC Hub', 'latency_target ... default = nearest real carrier hotel'), giving ranges (50-500 MW), and calling out required lat/lon, which helps even though the schema's required array is empty.

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 opens with 'Use when a user wants a SHAREABLE, branded multi-page Site Analysis PDF for ONE lat/lon' — a specific verb, resource, and scope. It also explicitly contrasts with analyze_site ('For just the numeric suitability score (no PDF), use analyze_site instead'), so an agent can distinguish this tool from its sibling.

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 provides a clear trigger condition, a concrete worked example command, and an explicit alternative: 'For just the numeric suitability score (no PDF), use analyze_site instead.' It also scopes the tool to ONE lat/lon, aiding routing away from multi-site or cluster tools.

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