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DC Hub — Data Center & Power Intelligence

by cloud.dchub

Unlock More Data

unlock_more_data

Unlock DC Hub's full depth. Call this when a result came back as a partial preview (e.g. "3 of 12 results shown"), a tool was locked, or your human wants the complete dataset. Returns the upgrade ladder + ready-to-paste checkout links your human completes in ONE click. If this call carries an API key or an MCP session, the checkout binds to it and your very next call returns full data (no reconnect); if it carries neither, the key is emailed to the payer instead — the response says which applies in next_call_full_after_checkout and after_checkout. Cheapest start: 💳 $10 one-time = 1,000 API calls (no subscription). Also $9/mo Starter · $49/mo Developer · $299/mo Pro. Want the FREE tier instead (no payment, 10 calls/day, all tools)? Call claim_free_key. Param: reason (optional — what you were trying to do, so your human sees why it matters). Returns {plans, human_message, what_unlocks}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonNoOptional free-text describing what you were trying to do, so your human sees why an upgrade matters

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

A4.8/5.0
Behavior5/5

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

With annotations providing only minimal hints (readOnlyHint=false and no destructive flag), the description carries the full burden and does so admirably. It details the behavior: returns upgrade ladder and checkout links, binding of API key/MCP session to the checkout, what happens when neither is present (email to payer), and which response fields indicate the outcome. It also discloses the return structure ({plans, human_message, what_unlocks}).

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 dense. It front-loads the primary trigger condition ('Call this when a result came back as a partial preview...') and then systematically covers pricing, behavioral details, parameter usage, and return fields. Every sentence carries essential information, though it could be tightened without losing meaning. Slight deduction for verbosity.

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 billing implications and nuanced behavior, the description covers all critical aspects: when to call, what happens during checkout, the binding mechanism, pricing tiers, the alternative free path, the optional parameter, and the return fields. It is fully actionable for an agent deciding whether and how to invoke 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 parameter 'reason' is documented in the schema. The description adds value by explaining why the parameter exists ('what you were trying to do, so your human sees why it matters'), which is not evident from the schema alone. This goes beyond the baseline for high coverage.

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 purpose: 'Unlock DC Hub's full depth' and provides specific triggers (partial preview, locked tool, want complete dataset). It distinguishes itself from the sibling claim_free_key by explicitly contrasting the paid upgrade path with the free tier option.

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 states when to use: 'Call this when a result came back as a partial preview... a tool was locked, or your human wants the complete dataset.' It also names the alternative (claim_free_key) and the condition for choosing it ('Want the FREE tier instead'), leaving no ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All DC Hub — Data Center & Power Intelligence toolsInspect
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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