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DC Hub — Data Center & Power Intelligence

by cloud.dchub

Get Gas Intelligence

get_gas_intelligence
Read-onlyIdempotent

Use when a human asks about gas-fired or behind-the-meter power economics for a data center in a US state — "is gas power cheaper than the grid in Texas?", "what is the gas access + pipeline situation in Virginia?". The GAS analogue of get_grid_intelligence: fuses the DC Hub Gas Index (DCGI), live Henry Hub, gas-to-grid $/MWh across heat-rate scenarios, pipeline-operator presence, and the live grid gas share into one per-STATE brief. Params: region (US state code or name, e.g. "TX" | "Texas" | "Virginia"). Returns: {region, region_name, gas_access (pipeline counts + operators — PRESENCE not firm capacity), henry_hub_usd_mmbtu (live), basis_usd_mmbtu (synthetic-labeled), delivered_price_usd_mmbtu (null where the tariff table is sparse — surfaced honestly, never fabricated), live_grid_gas_share_pct, pipeline_presence (operators + parent midstreams), data_basis (per-field provenance/confidence), omitted_no_fabrication, dcgi_status, gas_to_grid_status}. ★ ★ TWO DIFFERENT STATES, do not merge them. dcgi_score and dcgi_verdict were withdrawn 2026-08-08 and RESTORED 2026-08-30 once all three defective terms were repaired — they are returned again, but a figure published before 2026-08-08 is from a different index and must not be compared with one published now (/api/v1/dcgi/methodology corrections). gas_to_grid_usd_per_mwh and the behind-the-meter-vs-grid delta remain WITHDRAWN and are still NOT returned: five surfaces disagreed by up to 5.5x on the same market's $/MWh with no sanity gate, and that defect is not fixed. dcgi_status and gas_to_grid_status carry the current state of each — read them rather than assuming both moved together. DO NOT quote a cached DCGI score or $/MWh. Everything else in this brief — live Henry Hub, live ISO gas share, pipeline and parent-midstream presence — is unaffected and is what this tool is now for. Every field carries a data_basis label; gas storage / LNG / firm pipeline capacity are deliberately OMITTED (no feed). Do NOT use for electricity grid headroom (use get_grid_intelligence) or the DCGI score alone (use get_gas_index).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNoAlias for region — the US state code or name
regionNoUS state code or name (required), e.g. "TX", "Texas", "Virginia"

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
Behavior5/5

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

Very rich behavioral disclosure beyond annotations: it reveals the history of withdrawn/restored DCGI terms, warns that pre-2026-08-08 figures come from a different index and must not be compared, states that gas_to_grid_usd_per_mwh and the delta remain withdrawn due to a 5.5x defect, and insists on reading the status fields rather than assuming they move together. It also discloses deliberate omissions and the 'never fabricated' honesty policy.

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 well-organized and front-loaded with the use case before caveats. It uses clear labels (Params, Returns, warnings) and bold/star emphasis for critical warnings. There is minor redundancy in repeating the 'two different states' warning and the withdrawn/restored narrative, but each sentence carries substantive operational guidance.

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 and the rich output schema context, the description is exceptionally complete. It covers what fields are returned, per-field provenance, which fields are null and why, what is deliberately omitted, which status fields to read, and which sibling tools to use instead. Nothing an agent needs to call this safely and correctly appears to be missing.

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

Parameters3/5

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

The input schema already provides 100% description coverage for both parameters, so the baseline is 3. The description only repeats the region format ('US state code or name, e.g. "TX" | "Texas" | "Virginia"') without adding new meaning beyond the schema. The schema also has a 'state' alias that the description does not clarify, though this is not a significant gap.

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: 'Use when a human asks about gas-fired or behind-the-meter power economics for a data center in a US state'. It clearly differentiates itself from siblings by naming get_grid_intelligence as the GAS analogue and explicitly excluding grid headroom and DCGI-score-only use cases.

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 gives explicit when-to-use guidance with concrete example questions ('is gas power cheaper than the grid in Texas?') and explicit when-not-to-use guidance with named alternatives: 'Do NOT use for electricity grid headroom (use get_grid_intelligence) or the DCGI score alone (use get_gas_index).' This leaves no ambiguity about tool selection.

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