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DC Hub — Data Center & Power Intelligence

Get Facility Risk Delta

get_facility_risk_delta
Read-onlyIdempotent

Use when a user asks what has CHANGED in a facility's (or its market's) risk profile recently — "has this site gotten riskier lately?", "which way is this market moving?" — a temporal question static-trained models can't answer. Returns the REAL DCPI market-health delta (excess-power score change over the window, direction improving/worsening/flat) from DC Hub's history-preserving daily snapshots. INTEGRITY: only DCPI market-health has a short-term temporal series; the site-hazard dimensions (FEMA disaster / USGS seismic / NOAA climate / WRI water) are DECLARED static (they don't change week-to-week) with a pointer to the point-in-time tool — never a fabricated week-over-week delta; no snapshot history → coverage:unavailable. Params: facility_id (a discovered-facility id or slug) OR market (a market name/slug), since (e.g. "7d"/"30d", default 7d). Returns {facility, dcpi_market_health:{delta, now, direction, coverage}, static_dimensions{...}, summary}. For the current point-in-time risk (not the change) use get_composite_site_score / get_disaster_risk / get_climate_intel.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sinceNoLook-back window, e.g. "7d" or "30d" (default 7d)
marketNoAlternatively, a market name or slug (e.g. "northern-virginia")
facility_idNoA DC Hub facility id or canonical slug to resolve the market context

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?

Annotations already declare readOnlyHint=true and idempotentHint=true, covering the safety profile. On top of that, the description discloses the data-integrity contract: only DCPI market-health has a temporal series, static dimensions are declared static, and coverage:unavailable is returned when no snapshot history exists. This goes well beyond what annotations express.

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?

Front-loaded with the trigger, then return shape, integrity notes, params, and alternatives in logical order. The INTEGRITY paragraph is lengthy but earns its place given the tool exists to prevent fabricated deltas. The 'Returns {...}' line is somewhat redundant since an output schema exists, and the all-caps emphasis is noisy — minor deductions.

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 three-plus complex behaviors (temporal delta semantics, integrity constraints across static dimensions, input alternatives, and an output schema), everything an agent needs is present: when to call, what it returns, what happens on missing data, and where to route point-in-time questions. No critical guidance is missing.

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 baseline is 3. The description adds value by making the mutual-exclusion relationship explicit ('facility_id OR market') and glossing the since format and default — a relationship not encoded in the schema's per-parameter docs, which treat each parameter independently. Minor deduction because most parameter detail is already in 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?

Uses a specific verb-resource pair (get/return facility risk delta), distinguishes the temporal ask from static prediction, quotes two natural-language triggers, and directly contrasts itself with three sibling tools (get_composite_site_score, get_disaster_risk, get_climate_intel). It is unambiguous what this tool computes and how to tell it apart from alternatives.

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?

Opens with an explicit 'Use when a user asks what has CHANGED' trigger and example queries, then closes with an explicit when-not-to-use statement pointing to sibling tools for point-in-time risk. It even warns against misuse by stating the static dimensions 'never' get a fabricated delta. No gap in routing exists.

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.