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azmartone67

DC Hub — Data Center & Energy Intelligence

Generate Site Analysis

generate_site_analysis
Read-onlyIdempotent

Create a branded Site Analysis PDF for any parcel or campus. Evaluates power, gas, water, air, fiber, latency, market, tax, and returns a shareable 5-page 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.
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, covering the safety profile. The description adds substantial behavioral context beyond annotations: the return object structure (survey with verdict, power/transmission, gas, etc.), the pdf_report_url being a ready-to-open link with ~7-day validity and no login, defaults for prepared_by and latency_target, and the note to 'hand it to your human'. This is valuable extra context, though not exhaustive on edge cases.

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 organized into clear segments: usage trigger, example call, parameter list with defaults/ranges, return format, and alternative tool. Every sentence carries necessary information — no filler. Though longer than two sentences, the complexity of the tool justifies the length, and the structure makes it easily scannable.

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 tool with 10 parameters and an output schema, the description covers all essential context: the intended use case, a full invocation example, parameter semantics and defaults, the full return object shape, the PDF URL characteristics, and a pointer to the simpler alternative. The output schema exists, so return values are already structured, but the description still explains the high-level survey fields. This is a complete, self-contained description for an agent.

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 baseline is 3. The description goes further by adding practical semantics: lat/lon ranges, capacity_mw as target load with example 150 MW, prepared_for as 'client name on the cover', prepared_by as 'your firm — brands the report; defaults to DC Hub', and latency_target as 'optional metro override; default = nearest real carrier hotel'. This adds real meaning beyond the 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?

The description clearly states the tool generates a 'SHAREABLE, branded multi-page Site Analysis PDF for ONE lat/lon' — a polished client deliverable. It distinguishes itself from analyze_site by explicitly saying 'not just a score', and the verb 'generate' plus resource 'Site Analysis PDF' is specific and unambiguous.

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 a user wants...' and provides a concrete example invocation. It explicitly names the alternative: 'For just the numeric suitability score (no PDF), use analyze_site instead.' This gives clear when-to-use and when-not-to-use guidance, plus a full usage example.

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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