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DC Hub — Data Center & Power Intelligence

by cloud.dchub

Analyze Parcel

analyze_parcel
Read-onlyIdempotent

Structured read of a parcel BOUNDARY — pass your own GeoJSON Polygon/MultiPolygon, OR just lat+lon and DC Hub finds the containing parcel in its HOSTED parcel-boundary layer (free county/state GIS polygons, rolling out by data-center market — Loudoun County VA first; a point outside hosted coverage returns an honest 404 with the coverage list, never a guess). Returns _entity=parcel_analysis: geodesic total_acres, a per-member acreage breakdown, a contiguous flag, representative_point = the centroid of the LARGEST-area member (never the multi-part geometric center, which can land off-parcel on a highway median or river and poison every point-keyed read), and hosted_parcel {parcel_id, county, state, acres_per_source} when the polygon came from the hosted layer. Also returns a site_evaluation_handoff to pipe into analyze_site + get_water_risk at that anchor. Use when you HAVE a boundary or a point on a specific parcel and want it anchored + sized; for a general lat/lon site score use analyze_site; for the interconnection-queue survivor set use get_refined_queue (queue rows carry NO parcel identity, so they never auto-join to hosted parcels).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude of a point ON the parcel — used with lon when geometry is omitted to look up the containing parcel from the hosted county/state GIS layer
lngNoAlias for lon — either name works
lonNoLongitude of a point ON the parcel (used with lat when geometry is omitted)
geometryNoGeoJSON Polygon or MultiPolygon parcel boundary, e.g. {"type":"Polygon","coordinates":[[[lng,lat],[lng,lat],...]]} — a MultiPolygon carries discontinuous parcels as one envelope. Omit to look up the hosted parcel containing lat/lon instead
latitudeNoAlias for lat — either name works
longitudeNoAlias for lon — either name works
capacity_mwNoOptional target load in MW to pass through into the site_evaluation_handoff

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?

The description discloses read-only behavior (consistent with readOnlyHint), algorithmic details (representative_point uses centroid of largest member, never multi-part geometric center), and error semantics (honest 404). It adds valuable context beyond annotations, fully transparent about what the tool does and does not do.

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 but well-organized, with a single paragraph using semicolons to separate distinct ideas. It front-loads the core function and then expands on coverage, return fields, and usage. While slightly long, it avoids redundancy and each clause adds necessary detail, earning a high but not perfect score.

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 description is fully self-contained: it explains what the tool does, the input modes, coverage, error behavior, return fields, how to chain with other tools (site_evaluation_handoff), and when to use alternatives. It leaves no significant gaps for an agent to correctly invoke it.

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 rich descriptions for all parameters (coverage 100%), including aliases, geometry format, and optional capacity_mw. The tool description reiterates these points but does not add significant new meaning beyond the schema. It meets the baseline for high schema coverage but does not enhance parameter understanding further.

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 performs a structured read of a parcel boundary, accepts either a GeoJSON polygon or lat/lon coordinates, and explicitly distinguishes it from sibling tools (analyze_site, get_refined_queue) by naming use cases. It is precise and unambiguous.

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 provides explicit when-to-use guidance: 'use when you HAVE a boundary or a point on a specific parcel and want it anchored + sized', and contrasts with alternatives. It also discloses coverage limitations ('rolling out by data-center market — Loudoun County VA first') and error behavior ('returns an honest 404'), leaving no ambiguity about applicability.

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