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DC Hub — Data Center & Power Intelligence

by cloud.dchub

Industry News

get_news
Read-onlyIdempotent

FRONT DOOR CHECK — if news is only ONE input to a bigger question (is this market heating up, should we still build here), call execute_plan(intent="<the user's question, unchanged>") and let it pull news alongside the market and grid reads. If the user actually wants the headlines, get_news IS the right call — one round trip; do not send a plain news request through the planner. Curated data center industry news from 40+ trade sources (DCD, Data Center Knowledge, Data Center Frontier, Capacity Media, The Register Data Centre, Fierce Telecom, etc.) refreshed every 30 min. Returns title, summary, source, published_at, and the market/operator entities mentioned. Filter by category (deals/permits/outages/policy/AI). Answers "what is happening in the data center industry this week", "any news on AI capacity". Try: get_news category=AI limit=10. The parameter is category, not topic. Industry news only; do NOT use for structured M&A deal data (use list_transactions) or the construction pipeline (use get_pipeline).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (1-500; default varies by tool)
queryNoFree-text keyword to filter news, e.g. "Stargate" or "interconnection queue"
sourceNoRestrict to one trade source, e.g. DCD, "Data Center Frontier", "Capacity Media"
date_toNoLatest published date, ISO-8601 (YYYY-MM-DD)
categoryNoNews topic filter, e.g. deals, permits, outages, policy, AI
date_fromNoEarliest published date, ISO-8601 (YYYY-MM-DD)
min_relevanceNoMinimum relevance score 0-1 to include an item, e.g. 0.5

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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: refresh frequency ('refreshed every 30 min'), source list, and return fields. It also reinforces the read-only nature without contradicting annotations.

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?

The description is efficient and front-loaded: the most critical routing guidance appears first, followed by purpose, sources, return format, filters, example, and exclusions. Every sentence adds distinct value, and the structure supports quick scanning by an agent.

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 read-only news retrieval tool with an output schema, the description covers purpose, usage guidance, sources, refresh cadence, return fields, filtering, and exclusions. Nothing needed for correct invocation is missing; the presence of an output schema means return format need not be elaborated.

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 enhances parameter understanding by clarifying the category parameter ('Filter by category (deals/permits/outages/policy/AI)') and explicitly warning 'The parameter is `category`, not `topic`.' It also provides a concrete example ('get_news category=AI limit=10') that demonstrates usage.

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?

Description states a specific verb and resource: 'Curated data center industry news from 40+ trade sources... Returns title, summary, source, published_at, and the market/operator entities mentioned.' It clearly distinguishes from siblings by explicitly naming alternatives: 'do NOT use for structured M&A deal data (use list_transactions) or the construction pipeline (use get_pipeline).'

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?

Explicitly instructs when to use this tool vs. alternatives: 'If the user actually wants the headlines, get_news IS the right call — one round trip; do not send a plain news request through the planner.' Also provides a front-door check for when to route through execute_plan, and names specific exclusion conditions with sibling tool names.

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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