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azmartone67

DC Hub — Data Center & Energy Intelligence

Platform Health

get_backup_status
Read-onlyIdempotent

Check DC Hub platform health: confirm database backup integrity, data freshness across 49 sources, and agentic heartbeat score before relying on it in production.

Instructions

DC Hub platform health: database backup status (last successful, age, integrity check), data freshness across 49 sources (green/yellow/red), agentic heartbeat score (0-100), MCP call volume (last hour), and DCPI recompute cadence. Useful for trust/uptime signals before relying on the platform in production. Try: get_backup_status. Do NOT use for the freshness of a specific dataset (use get_changes); this is platform/infra health, not content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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.
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive hints. The description adds valuable behavioral context by detailing the specific metrics returned (e.g., 49 sources, green/yellow/red, 0-100 score, last hour) and the scope of platform health. No contradictions with 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 front-loaded and information-dense. However, the phrase 'Try: get_backup_status' is redundant because the agent already knows the tool name, adding slight waste. Otherwise every sentence earns its place.

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?

With no parameters, rich annotations, and an output schema present, the description covers purpose, scope, usage boundaries, and even misuse warnings with a named alternative. It is fully self-contained for an agent.

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?

This tool has zero parameters, and the input schema is empty. Following the instruction that 0 params baseline is 4, the description doesn't need to provide parameter details. Nothing is missing.

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 it reports DC Hub platform health, enumerating specific components: database backup status, data freshness, agentic heartbeat, MCP call volume, and DCPI recompute cadence. It explicitly distinguishes from get_changes by excluding dataset-level freshness, making the tool's scope unmistakable.

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 says 'Useful for trust/uptime signals before relying on the platform in production' and warns 'Do NOT use for the freshness of a specific dataset (use get_changes)'. This gives clear when-to-use and when-not-to-use guidance, naming the alternative tool.

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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