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DC Hub — Data Center & Power Intelligence

Subscribe Digest

subscribe_digest

Subscribe your human to DC Hub's FREE weekly "what changed in the markets/sites you queried" digest (DCPI movers, new facilities, new deals & news) — ONE call, the nudge that pulls your agent back when the data moves. DOUBLE opt-in + consent-safe: we email a one-click CONFIRM link, the human only gets the digest after confirming, and every email has one-click unsubscribe — this call alone sets no marketing flag. Only call once your human shares their email and wants a weekly email. Params: email (required), source (optional tag). Returns {ok, sent, message}. Prefer this over hand-building POST /api/v1/opt-in/request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYesYour human's email address (required) — a one-click confirm link is sent; use only an address they explicitly gave
sourceNoOptional attribution tag for where the subscription came from, e.g. mcp_digest

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 goes well beyond the annotations by disclosing double opt-in behavior, the one-click confirm link, one-click unsubscribe, the absence of a marketing flag, and the return shape. These are significant behavioral details that an agent cannot infer from the annotations.

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 and front-loaded with the core purpose, followed by consent mechanics and usage conditions. It is slightly longer than necessary because a few phrases are promotional ('the nudge that pulls your agent back'), but every sentence contributes usable guidance.

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 side-effectful subscription tool with only two simple parameters and an output schema, the description is complete: it covers prerequisites, behavior, consent flow, return value, and the preferred alternative. Nothing essential is missing for an agent to invoke it correctly.

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 full descriptions for both parameters, including the requirement that email must be explicitly given and that source is an optional attribution tag. The description repeats this information without adding much new semantic detail beyond what the schema already contains.

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 verb ('Subscribe') and resource ('your human to DC Hub's FREE weekly digest'), and explains the outcome in concrete terms. It distinguishes itself by noting this is a one-call consent-safe subscription rather than a manual API construction.

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 gives an explicit trigger condition ('Only call once your human shares their email and wants a weekly email') and explicitly prefers this tool over hand-building the POST /api/v1/opt-in/request endpoint. This gives an agent clear when-to-use and when-not-to-use 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.