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DC Hub — Data Center & Power Intelligence

by cloud.dchub

Renewable Energy

get_renewable_energy
Read-onlyIdempotent

FRONT DOOR CHECK — if the question pairs renewables with siting, grid headroom or a market shortlist ("where can I put a 24-7-CFE campus"), call execute_plan(intent="<the user's question, unchanged>") instead. If you want one state's renewable fuel mix or PPA sizing input on its own, get_renewable_energy IS the right call — one round trip, and routing it through the planner would only cost steps. Use when siting a renewable-powered data center, sizing a PPA, reading one US state's utility-scale fuel mix, or assessing RE100/24-7-CFE feasibility. Example: "What is Texas wind+solar capacity and how much utility-scale solar is operating today?" — get_renewable_energy energy_type=solar state=TX. Params: energy_type one of "solar" | "wind" | "combined" (omit for all); state 2-letter US code (e.g. TX, VA, AZ); lat+lon (optional) for the nearest projects within 50mi. Returns: {capacity_mw_total, by_fuel: {solar_utility, solar_rooftop, wind_onshore, wind_offshore}, capacity_factor_pct, top_projects[{name, mw, operator, cod}], state_rps_target_pct, source: "EIA-860 + state RPS"}. Do NOT use for live grid generation (use get_grid_data) or non-US (use get_grid_scoreboard for EU/UK/AU/TW).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoOptional latitude in decimal degrees (-90 to 90) to find nearest projects within 50mi
lngNoAlias for lon — either name works
lonNoOptional longitude in decimal degrees (-180 to 180) to find nearest projects within 50mi
stateNoUS state abbreviation, e.g. TX, VA, AZ
latitudeNoAlias for lat — either name works
longitudeNoAlias for lon — either name works
energy_typeNoRenewable type: "solar", "wind", or "combined"; omit for all

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

A4.6/5.0
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, so the tool's safety profile is fully covered. The description adds transparency about geographic scope (US only), data source (EIA-860 + state RPS), and the fact that the 'combined' energy_type aggregates by fuel. The one gap is that it doesn't mention rate limits or caching, but for a read-only, idempotent tool with the data source stated, this is already strong. The annotations and description are fully consistent.

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 dense and front-loaded with the most important decision rule. It packs a lot of useful information into a compact form and delivers high-value content per word. The minor deduction is that the opening 'FRONT DOOR CHECK' is a bit theatrical, and the list of siblings to avoid is long — this could be tightened without losing substance. But overall, every sentence earns its place.

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?

The tool has an output schema, so the description needn't and does not explain return values. It covers: the coin-flip decision between this tool and execute_plan, when to use direct call vs. alternatives, the data source, the geographic scope, and the parameter usage. For a read-only, idempotent tool with full schema coverage and a full output schema, this is complete. An agent has everything needed to decide and call correctly.

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 the baseline is 3. The description raises it a notch by adding critical semantics the schema lacks: it explains that 'energy_type' should be omitted for 'all' (the schema only says 'omit for all' but the description clarifies the syntax), that lat+lon finds 'nearest projects within 50mi', and that 'lng' is an accepted alias. It also gives a worked example of how parameters map to a question. The explanation of aliases is particularly valuable given 7 parameters.

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 does more than state a verb and resource — it explicitly frames the tool as the 'FRONT DOOR CHECK' for renewables questions, lists concrete use cases (siting a renewable-powered data center, sizing a PPA, reading a US state's fuel mix, assessing RE100/24-7-CFE feasibility), and gives a worked example that maps a natural language question to parameters. This distinguishes it clearly from siblings like get_grid_data, get_grid_scoreboard, and execute_plan, and leaves no doubt about what the tool returns.

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 is exemplary here. It states exactly when to use the tool (one state's fuel mix, PPA sizing input, siting assessments) and when NOT to — pairing renewables with siting/grid-headroom questions should go to execute_plan, live grid generation goes to get_grid_data, and non-US regions go to get_grid_scoreboard. It even explains the cost trade-off (routing through the planner only costs steps), which is precisely the kind of decision guidance an agent needs.

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