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azmartone67

DC Hub — Data Center & Energy Intelligence

Generate Site Analysis

generate_site_analysis
Read-onlyIdempotent

Turn any lat/lon into a branded Site Analysis PDF, evaluating power, transmission, gas, water, air, fiber, latency, market, and tax. Get a shareable client-ready report link.

Instructions

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.
Install Server

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, idempotentHint=true, and destructiveHint=false, and the description does not contradict these. It adds valuable behavioral detail: the PDF link is ready-to-open, requires no login, is valid ~7 days, and the report structure via the return object. This exceeds the minimum needed given 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 long but every sentence contributes: when-to-use, an example, parameter semantics, return shape, and alternative routing are all packed in. It is front-loaded with the core purpose and example, though the parameter list could be more readable as structured bullets.

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 (10 params, no required, output schema present), the description covers all necessary aspects: input aliases, defaults, return object shape (survey + pdf_report_url), link validity, and when not to use it. The presence of an output schema further reduces the need to document return values, and the description complements rather than repeats it.

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: it highlights that lat/lon are required (even though the schema marks all params optional), gives a typical capacity range (50-500 MW), and explains default behavior for prepared_by ('defaults to DC Hub') and latency_target ('default = nearest real carrier hotel'). It also clarifies aliases (lng/latitude/longitude).

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 a specific verb and resource: 'generate a SHAREABLE, branded multi-page Site Analysis PDF for ONE lat/lon'. It explicitly contrasts with 'not just a score' and later names the sibling analyze_site, so an agent can immediately distinguish it from related tools.

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 states exactly when to use it ('when a user wants a SHAREABLE... PDF'), provides a concrete example, and closes with the exclusion: 'For just the numeric suitability score (no PDF), use analyze_site instead.' No inference is needed.

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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