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DC Hub — Data Center & Energy Intelligence

Set Shortlist Alert

set_shortlist_alert

Set a drift alert on a saved shortlist to get notified when a site's national standing drops materially. Stop polling and receive webhook or email alerts after daily baseline refresh.

Instructions

Set a DRIFT ALERT on a saved shortlist so you can stop polling and be notified when a site's national standing moves materially (Phase 5). Fires when any site in the shortlist has current percentile score < percentile_below OR score_delta_since_saved < delta_below (e.g. -8 = dropped 8 points vs when saved). Evaluated after each daily baseline refresh; delivers via webhook and/or email. This is the "wake me when it matters" loop for long-running siting campaigns. Scoped to your API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notifyYesDelivery: {"webhook":"https://..."} and/or {"email":"you@co.com"} — at least one required
delta_belowNoFire if any site's score_delta_since_saved drops below this — pass a NEGATIVE number, e.g. -8 (dropped 8+ points since saved)
shortlist_nameNoThe shortlist to monitor (created via save_to_shortlist)
percentile_belowNoFire if any site's current percentile objective_score drops below this (e.g. 70)

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.
Behavior5/5

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

The description goes beyond annotations by detailing trigger conditions (percentile_below and delta_below), evaluation cadence (after each daily baseline refresh), delivery channels (webhook and/or email), and scope (API key). It even explains the alert logic with an OR condition and an example. Annotations already indicate a non-read-only, non-destructive operation, so the added context is valuable.

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 every sentence serves a purpose: it defines the alert, explains triggers, gives an example, and states the use case. It is slightly longer than strictly necessary but remains focused and well-structured.

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 output schema and annotations, the description covers the essential behavioral aspects: what triggers the alert, when it is evaluated, how it is delivered, and its scope. It also implies the prerequisite of an existing shortlist via the shortlist_name parameter. No critical gaps are apparent.

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 the baseline is 3. The description adds semantic value by explaining how delta_below and percentile_below interact via the OR condition, and clarifies the negative-number convention for delta_below. This goes beyond the schema's individual 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 the action: 'Set a DRIFT ALERT on a saved shortlist' and explains the purpose (stop polling, get notified on material movement). It distinguishes from sibling tools like set_site_alert and set_market_alert by focusing on shortlist-level monitoring.

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 clear context for use (long-running siting campaigns, replacing polling) with 'This is the "wake me when it matters" loop'. However, it does not explicitly mention alternatives or exclusions, though the sibling tool names imply distinctions.

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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