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DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, Fiber

by cloud.dchub

Interconnection Queue

get_interconnection_queue
Read-onlyIdempotent

ISO interconnection queue snapshot: total queued GENERATION capacity (queued_load_total_gw, GW) per ISO from each ISO's public queue. For ERCOT it ALSO returns the large-load (data-center-driven) interconnection queue in queued_load_data_center_gw — >225 GW in process / ~9 GW approved-to-energize (ERCOT's published Q1-2026 figure; ERCOT is the only ISO that publishes a comparable large-load feed, so other ISOs' data_center_gw is null), with provenance in top_subregions. Sources: ERCOT GIS + Large Load Integration, PJM/MISO/SPP/CAISO/NYISO/ISO-NE public queues. Pass iso=ERCOT (or any of 7) to drill down. ★ The projects field CHANGES SHAPE with the call: with iso= it is an ARRAY of per-project rows; with iso omitted it is the all-ISO SUMMARY OBJECT {total, tracked, by_iso_count, top, note} and carries no per-project rows — check the type before indexing. Use for queue-depth site-selection and AI/data-center-load saturation intel (the ERCOT 225 GW number is the headline large-load figure no other source surfaces machine-readably). Do NOT use for a single-site time-to-power read (use get_grid_intelligence) or forward-looking emergence (use grid_transition_radar); this is the ISO-level queue snapshot.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
isoNoISO/RTO grid region to drill into: ERCOT, PJM, MISO, CAISO, SPP, NYISO, ISONE; omit for the all-ISO snapshot

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
vNoVerification flag for the snapshot
isoNoISO/RTO this snapshot covers (per-ISO drill-down form)
as_ofNoQueue snapshot date
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.
projectsNoSHAPE DEPENDS ON THE CALL: with iso= this is an ARRAY of queued generation projects (largest / most recent first); with iso omitted it is the all-ISO SUMMARY OBJECT {total, tracked, by_iso_count, top, note} — per-project rows are not returned for the all-ISO snapshot. Check the type before indexing.
provenanceNoCollection-level provenance block: {source, method, as_of, verification_counts, cite_url_template, license, cite_as}. Quote the verification level when citing.
source_urlNoQueue source URL
_front_doorNoIn-band front-door hint (first workflow-entry tool of a session): call plan_query(intent) first for the ordered multi-step plan.
source_nameNoQueue source name
_return_loopNoSuggested next-session delta call (get_changes since=24h) so you pull only what changed.
project_countNoProjects in the queue snapshot
top_subregionsNoProvenance / sub-region breakdown for the large-load figure (ERCOT)
queued_load_total_gwNoTotal queued GENERATION capacity in this ISO, GW
new_applications_q_gwNoNew queue applications in the latest period, GW (when published)
new_applications_periodNoPeriod the new-applications figure covers
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.
queued_load_dc_share_pctNoERCOT only: data-center share of queued load, %
historical_completion_pctNoShare of queued projects that historically complete, % (when published)
queued_load_data_center_gwNoERCOT only: large-load (data-center-driven) queue, GW — null for ISOs that publish no comparable feed

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already mark the call read-only and idempotent, and the description adds valuable behavioral details beyond them: the 'projects' field changes shape ('ARRAY per-project rows' vs 'SUMMARY OBJECT'), and ERCOT's data_center_gw is non-null only for ERCOT. This gives the agent important runtime expectations without contradiction.

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 long but every sentence carries operational information: purpose, sources, shape-shifting warning, use cases, and exclusions. It is front-loaded with the core purpose and uses a highlighted note for the critical shape change. Some promotional language ('headline large-load figure') adds slight noise but does not undermine clarity.

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 an ISO-queue snapshot tool with one optional parameter and an output schema available, the description covers all necessary context: data sources, regional coverage, return-shape caveat, and when to avoid. Nothing essential for correct selection and invocation is missing.

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% and the parameter description already explains omission behavior. The tool description adds value by clarifying the shape-changing impact of including or omitting iso ('with iso= it is an ARRAY... with iso omitted it is the SUMMARY OBJECT'), which is not in the schema itself. This is a meaningful param-related behavioral detail.

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 names a specific verb and resource: 'ISO interconnection queue snapshot' with clear scope ('total queued GENERATION capacity per ISO'). It also differentiates from siblings by explicitly naming alternatives like get_grid_intelligence and grid_transition_radar, so an agent can select this tool confidently.

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?

It gives explicit use cases: 'Use for queue-depth site-selection and AI/data-center-load saturation intel' and explicit exclusions: 'Do NOT use for a single-site time-to-power read (use get_grid_intelligence) or forward-looking emergence (use grid_transition_radar).' This is exemplary when-to-use vs alternatives guidance.

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 Site Selection & Colocation: Electricity, Power Grid, Gas, Fiber toolsInspect
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TDQS

A4.1/5.0
Disambiguation2/5

Multiple tools occupy nearly identical semantic space: search/search_facilities/search_intelligence/semantic_search all retrieve data, and analyze_site/compare_sites/score_facility/get_composite_site_score/rank_sites all score or rank locations. Despite extensive disambiguation in the descriptions, the boundaries are subtle enough that an agent will likely misroute queries. Account and meta tools (claim_free_key, bind_email, unlock_more_data, subscribe_digest, recover_my_key, execute_plan, plan_query, discover_tools) add further selection noise.

Naming Consistency4/5

The vast majority of tools follow a clear snake_case verb_noun convention (get_*, search_*, list_*, set_*, save_*, compare_*, analyze_*, rank_*, plan_*). A few noun-phrase names break the pattern (deal_autopsy, hyperscaler_deals, grid_transition_radar, site_selection_canvas, standing_intent, ai_capacity_index), but these are still readable and discoverable.

Tool Count1/5

83 tools is an extreme count for any server, far beyond the 25+ threshold that already signals bloat. The domain is broad, but the catalog is inflated by overlapping variants, multiple meta-tools (execute_plan, plan_query, discover_tools, get_agent_registry, get_backup_status, summarize_for_citation), and account/upgrade plumbing (claim_free_key, bind_email, recover_my_key, unlock_more_data, subscribe_digest). This imposes heavy context and selection costs on agents.

Completeness5/5

The data-center siting domain is covered exhaustively: single-site scoring, market ranking, grid/gas/fiber/water/climate/disaster/tax/permitting intelligence, interconnection queues, construction pipeline, deals/news, saved-site monitoring, alerts, research dossiers, and citation support. Every workflow has a continuation path, so agents will not hit dead ends.

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