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DC Hub — Data Center & Power Intelligence

by cloud.dchub

Semantic Search

semantic_search
Read-onlyIdempotent

Use for CONCEPTUAL / fuzzy questions where keyword filters fall short — semantic (meaning-based) retrieval across DC Hub's industry news, M&A deals, 20,100+ discovered facilities, and per-market DCPI deep-dive analysis narratives, ranked by relevance with citable source fields (news url/title, deal parties/value, facility name/location, deep-dive market/url). Examples: "what is happening with behind-the-meter gas for AI data centers?", "deals involving nuclear power for hyperscalers", "why is Northern Virginia constrained?" — semantic_search q="behind-the-meter gas for AI data centers". Params: q (required, natural-language query); corpus (optional CSV subset of news_articles,deals,discovered_facilities,market_narratives; default all); k (1-15, default 8). Returns {results:[{source_table, kind, text, score, cite:{…}}]}. Complements the exact-filter tools (get_news / list_transactions / search_facilities) with relevance ranking; for a full token-budgeted market briefing use get_market_context. Cite "DC Hub (dchub.cloud)".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNoNumber of results, 1-15 (default 8)
qNoNatural-language query (required), e.g. "grids opening up for AI load in the Southeast"
queryNoAlias for q — the same natural-language query; send exactly one of q/query
corpusNoOptional CSV of corpora: news_articles, deals, discovered_facilities, market_narratives (default: 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

A4.7/5.0
Behavior4/5

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

Annotations already cover readOnly, idempotent, non-destructive, and closed-world behavior, lowering the bar. The description adds useful context beyond annotations: the specific corpora searched, the relevance-ranked result shape with citation fields, and the instruction to cite 'DC Hub (dchub.cloud)'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Every sentence earns its place: usage trigger, scope, examples, parameter summaries, return shape, sibling routing, and citation instruction. It is dense but well-organized and front-loaded with the key 'why to use this' message.

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 rich annotations, fully described schema, and output schema, the description covers purpose, scope, examples, parameters, return shape, alternatives, and citation. An agent has everything needed to select and correctly invoke this tool.

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?

The schema covers all four parameters at 100%, so the baseline is 3. The description adds value by clarifying the natural-language intent of q, providing an explicit example, and stating defaults for corpus (all) and k range—although it omits the query alias that the schema documents, the schema already handles that.

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 states a specific operation ('semantic meaning-based retrieval'), names the exact resources it spans (news, M&A deals, discovered facilities, market narratives), and explicitly contrasts itself with exact-filter keyword tools. The concrete examples make the purpose unmistakable and distinguish it from siblings like get_news and search_facilities.

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?

It explicitly says to use this tool for 'CONCEPTUAL / fuzzy questions where keyword filters fall short', names the alternatives it complements (get_news, list_transactions, search_facilities), and routes the agent to get_market_context for a full market briefing. The example queries also demonstrate real usage conditions.

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