Skip to main content
Glama

DC Hub — Data Center & Power Intelligence

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

TDQS

A5/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description discloses important dynamic behavior: the `projects` field changes shape depending on whether `iso` is provided, ERCOT is the only ISO with non-null `data_center_gw`, and the response includes provenance in `top_subregions`. This is valuable context that annotations cannot convey.

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

Conciseness5/5

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

The description is long but densely packed with essential information. It front-loads the core purpose, then adds critical shape-change caveats, usage boundaries, and alternative routing. There is no fluff; each sentence contributes to correct invocation or interpretation.

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 a single optional parameter, a rich output schema, and annotations covering safety and idempotency, the description fully covers all needed context: purpose, scope, behavioral quirks, data source caveats, and explicit do/don't usage. No significant gap remains for an agent to call this correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although the schema already documents the `iso` parameter at 100% coverage, the description adds crucial semantics: omitting `iso` returns an all-ISO summary object, while including it returns an array of per-project rows. It also enumerates the valid ISO values and highlights the special ERCOT behavior, significantly enriching the parameter's meaning.

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 identifies the tool as an ISO-level interconnection queue snapshot with a specific verb ('snapshot') and resource, and it explicitly distinguishes itself from related tools like get_grid_intelligence and grid_transition_radar. It also explains the unique ERCOT large-load data center field, making the tool's purpose 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?

The description explicitly states when to use this tool ('queue-depth site-selection and AI/data-center-load saturation intel') and when not to use it ('single-site time-to-power read' or 'forward-looking emergence'), naming the exact alternative tools. This removes ambiguity about tool selection.

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