Skip to main content
Glama

DC Hub — Data Center & Power Intelligence

by cloud.dchub

Suggest Reallocation

suggest_reallocation
Read-onlyIdempotent

When a saved site DRIFTS (its national standing dropped — surfaced by get_shortlist refresh or a set_shortlist_alert firing), get replacement candidates from the rest of that shortlist so the alert becomes an action, not just a warning (Phase 5). Returns TWO tiers — tier_1_same_region (a near-in tactical swap) and tier_2_cross_region (a different-region arbitrage) — each re-scored against the DRIFTED slot's own objectives, PLUS drift_is_systemic: if the rest of your shortlist also slipped, the drop is region/baseline-wide and a same-region swap will inherit it (prefer cross_region); if peers held, it's idiosyncratic (tactical_ok). DC Hub does the reduction; the final weighted pick is yours. Candidates come from THIS shortlist only (save more via save_to_shortlist to widen the pool). Scoped to your API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
shortlist_nameYesThe shortlist to re-allocate within (created via save_to_shortlist)
drifted_site_refNoOptional site_ref of the drifted slot to replace; if omitted, the current lowest-scoring site is treated as the drifted one

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

A4.3/5.0
Behavior5/5

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

Annotations already mark the tool read-only and idempotent, and the description adds substantial behavioral context: the two-tier output (tier_1_same_region and tier_2_cross_region), the drift_is_systemic decision logic, the fact that DC Hub performs the reduction, and API-key scoping. It also implies non-mutating behavior by saying 'the final weighted pick is yours.'

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

Conciseness3/5

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

The description is long and dense, with multiple parenthetical clauses and embedded explanations that make it less scannable than ideal. All information is relevant, but a cleaner structure with front-loaded trigger and output essentials would improve readability.

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 presence of an output schema and annotations, the description is highly complete: it covers the trigger condition, output tiers, decision logic, pool constraint, and API-key scope. Nothing an agent needs to decide whether to call this tool and what to expect is missing.

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?

Schema description coverage is 100%, so the baseline is 3; both parameters already have detailed descriptions in the schema. The main description reinforces the shortlist_name meaning by explaining the candidate-pool constraint, but it does not add new parameter-level facts beyond what the schema documents.

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 verb ('get replacement candidates') on a specific resource ('the rest of that shortlist') when a saved site drifts. It also distinguishes from related sibling tools by naming the trigger events (get_shortlist refresh, set_shortlist_alert) and by explaining the output tiers.

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 clearly defines when to use the tool (when a saved site drifts) and how that drift is surfaced. It also gives an implicit exclusion by noting candidates come only from 'THIS shortlist' and that widening the pool requires save_to_shortlist, which points to an alternative without explicitly saying 'do not use X'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All DC Hub — Data Center & Power Intelligence toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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