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DC Hub — Data Center & Power Intelligence

by cloud.dchub

Save To Shortlist

save_to_shortlist

Save a site into a PERSISTENT, named shortlist that survives across conversations (Phase 5 statefulness). Snapshots the site's objectives + its current percentile objective_score, so you can re-score it later against the evolving national baseline. Use to build a durable siting shortlist across days/weeks; the list is scoped to your API key. Pair with get_shortlist to re-score + see drift. MINIMAL call: save_to_shortlist(shortlist_name="my-targets", site={site_ref, lat, lng, capacity_mw}) — objectives are optional. If you DID rank the site (analyze_site / rank_sites), pass those metric fields inside site and your objectives map too, and the re-scoring reuses them. Requires an API key so the list is private to you and survives to your next conversation: call claim_free_key first if you have none.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
siteYesSite object. MINIMAL form is enough: {site_ref, lat, lng, capacity_mw}. Richer is better — add any analyze_site metric fields (risk_resilience, fiber_connectivity, water score…) and those become what gets re-scored later.
notesNoOptional free-text note, e.g. "strong fiber, acceptable water"
objectivesNoOPTIONAL {field: signedWeight} map (+maximize/-minimize) if this site was ranked under explicit objectives — stored so re-scoring reuses the same criteria. Omit it and DC Hub weights the site's own metric fields equally.
shortlist_nameYesName of the shortlist, e.g. "Q3-2026-1GW-targets" — created if new. REQUIRED.

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

A4.3/5.0
Behavior4/5

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

Annotations are all false, so the description carries the burden of disclosure. It transparently covers persistence across conversations, API-key scoping, snapshotting of objectives and scores, and the effect of omitting objectives (equal weighting). No contradictions with annotations; the tool is clearly not read-only or destructive.

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 fairly long but every sentence adds information. It starts with the core action, then explains persistence, provides a minimal call example, and covers prerequisites. The structure is logical: action, persistence, pairing, minimal input, richer input, API key requirement. No fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (optional objectives, re-scoring, persistence) and that an output schema exists, the description covers all essential aspects: requirements, scope, and integration with other tools. It would benefit from noting the output format or side effects, but the output schema likely covers that; this is sufficient.

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 parameters are already documented. The description adds value by explaining the minimal vs richer forms of 'site', how objectives are stored and reused, and the implications of omitting them. It clarifies optionality and re-scoring behavior beyond the schema's basic field descriptions.

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 a specific action (save a site into a named shortlist) and the resource, while distinguishing it from siblings like save_site and get_shortlist. It also notes the persistence and scoping, making the purpose unambiguous for an agent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use context: building durable shortlists, pairing with get_shortlist, and calling claim_free_key if no API key. It references alternatives implicitly via 'Pair with' and 'call claim_free_key', but doesn't spell out when NOT to use it or compare to save_site directly. Still strong guidance.

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.

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