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DC Hub — Data Center & Power Intelligence

by cloud.dchub

Save Site

save_site

NEEDS A KEY (free): this WRITES to your account, so a keyless call returns auth_required — call claim_free_key FIRST (one step, no email) if you have none. Save a candidate data-center site to your DC Hub account to track it across sessions. Give lat + lon (plus optional name, state, market, target_mw, notes). Returns the saved site id. Pass market and DC Hub snapshots the site's DCPI baseline at save time, so every later list_saved_sites / get_changes shows how ITS score and verdict moved since you saved it. Builds a persistent shortlist an agent can revisit + monitor — after saving, pass the returned id to set_site_alert so DC Hub emails you when that site’s DCPI/capacity/nearby-facilities move (no re-checking). Answers "remember this parcel for me", "keep this candidate so I can come back to it next session". Try: save_site lat=39.04 lon=-77.48 name="Ashburn parcel" market=northern-virginia target_mw=100. Do NOT use to read back the shortlist (use list_saved_sites), download it (use export_dataset), or score a site (use score_facility); this WRITES one site to your account.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoSite latitude in decimal degrees (-90 to 90), e.g. 39.04
lngNoAlias for lon — either name works
lonNoSite longitude in decimal degrees (-180 to 180), e.g. -77.48
nameNoOptional label for the saved site, e.g. "Ashburn parcel"
notesNoOptional free-text notes to store with the saved site
stateNoUS state abbreviation for the site, e.g. VA
marketNoMarket slug (metro) the site belongs to, e.g. northern-virginia
latitudeNoAlias for lat — either name works
longitudeNoAlias for lon — either name works
target_mwNoTarget power load for the planned build in megawatts (MW), e.g. 100

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

A5/5.0
Behavior5/5

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

Annotations already mark readOnlyHint=false, but the description adds critical behavioral context: it WRITES to the account, keyless calls return auth_required, it returns the saved site id, and it snapshots the DCPI baseline at save time. It also explains the alert linkage via set_site_alert. This far exceeds what annotations provide, with no contradiction.

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 long, every sentence earns its place: the key requirement is front-loaded, the purpose and return value follow, then the market nuance and exclusions. The structure is logical, uses an example call, and has no fluff. It is dense but efficient.

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 parameters, write operation, auth requirement, side effects like baseline snapshot and alert setup), the description covers all essential facets: action, parameters, return value, prerequisites, downstream usage, and sibling routing. An agent has everything needed to invoke it correctly without additional lookups.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for all 10 parameters, yet the description adds meaning beyond the schema: the special effect of `market` (triggers DCPI baseline snapshot), the aliasing between lat/lon and latitude/longitude, and a concrete example call. The semantics of `market` are not obvious from the schema alone, making this genuinely additive.

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 saves a data-center site to the user's DC Hub account, with a specific verb and resource. It explicitly distinguishes itself from reading (list_saved_sites), downloading (export_dataset), and scoring (score_facility) by name, so an agent can easily differentiate across the large sibling set.

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 gives explicit when-to-use guidance (remember a parcel, track across sessions), prerequisites (call claim_free_key first if you lack a key, with a one-step hint), and clear when-not-to-use exceptions with named alternatives. This is textbook usage 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