DC Hub — Data Center & Power Intelligence

Plan Query

plan_query

Read-onlyIdempotent

INSPECT-ONLY — returns the plan WITHOUT running it. For a real multi-step DC Hub question call execute_plan(intent="...") instead: it uses the SAME deterministic no-LLM planner and then RUNS the sequence server-side, returning the answers in one envelope. Reach for plan_query only to review, log, diff or audit a plan before executing it yourself. Deterministic keyword routing over the tool registry — no LLM, no network, same intent always returns the same plan (free). Returns _entity=query_plan {best_tool, intent_confidence + workflow_confidence (dual 0-1: question-read vs executability), reason, planner_rationale, recommended_sequence:[{step, tool, depends_on, estimated_calls, why, args_hint}], execution_waves (steps grouped into concurrency waves), execution_strategy.parallel_groups, execution_estimate {estimated_calls, estimated_latency_ms, parallelizable}, alternatives (each with when + rejected_because), coverage_notes, matched_classes} plus a versioned replay (schema_version 1): planner_version, decisions:[{id, step, kind, status, decision, rationale, decision_confidence, depends_on}], rejected:[{id, tool, reason}], execution_graph:{waves, parallel_groups} — auditable and machine-readable, safe to log and diff across versions. args_hint values in come from the named earlier step — substitute them, never invent them. Pass structured hints via context (lat/lon, iso, market, capacity_mw, candidate_id, state, since) to sharpen the plan. For a family-level browse use discover_tools. This tool plans — it never executes; tools/list stays canonical for schemas.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`intent`	Yes	Natural-language description of what you are trying to find out, e.g. "rank markets for a 200MW AI campus" or "how much power is available in ERCOT"
`context`	No	Optional structured hints: {lat, lon, iso, market, capacity_mw, candidate_id, state (2-letter), since} — sharpens args_hint values and routing (e.g. lat/lon boosts the site-analysis route)

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	No	true when the intent was routed
`note`	No	Router disclaimer — deterministic keyword routing, tools/list stays canonical
`quota`	No	Caller quota state (remaining calls, tier) when available.
`intent`	No	The natural-language intent that was routed (echoed back)
`reason`	No	Why the router chose best_tool — the matched keywords / context signals
`replay`	No	FIRST-CLASS VERSIONED replay object (r-planner-v5.1, ChatGPT schema review): the planner's auditable decision trail — routing + per-step selection + rejections + concurrency graph, each decision with a stable id + status, keyed by planner_version so an agent can cite "Decision D2 selected rank_markets because…" and downstream tooling survives planner upgrades.
`_entity`	No	Payload class discriminator (e.g. facility\|market\|iso_grid\|queue_results\|deal\|report\|response) — branch on this before parsing the rest.
`chaining`	No	Zero-drift chaining guidance (candidate_id contract) when the plan crosses get_refined_queue → analyze_site / rank_sites
`citation`	No	Machine-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.
`best_tool`	No	The single best first tool to call for this intent (exact name from tools/list)
`confidence`	No	Deterministic router confidence, 0-1 — same intent always yields the same score; low values mean the intent was ambiguous (check alternatives). Alias of intent_confidence (v1 back-compat).
`provenance`	No	Collection-level provenance block: {source, method, as_of, verification_counts, cite_url_template, license, cite_as}. Quote the verification level when citing.
`_front_door`	No	In-band front-door hint (first workflow-entry tool of a session): call plan_query(intent) first for the ordered multi-step plan.
`_return_loop`	No	Suggested next-session delta call (get_changes since=24h) so you pull only what changed.
`alternatives`	No	Adjacent tools for nearby intents, including runner-up intent classes
`intent_class`	No	The matched intent class (market_ranking \| capacity_search \| market_comparison \| grid_headroom \| interconnection_queue \| hosting_capacity \| water_climate \| site_analysis \| deals_ma \| fiber_power_pairing \| fiber \| price \| incentives_tax \| power_timeline \| changes_delta \| facility_search \| unknown)
`routing_hint`	No	ADVISORY router: collapses 83 tools to one starting point, then names what lies outside DC Hub entirely. Deliberately carries no tool list, latency promise, confidence score, execution graph or planner version — those ride `replay` AFTER routing. Four fields specified by ChatGPT in the 2026-08-29 partner round; external_sources_recommended added on its own request in the 2026-08-30 briefing, because a source we do not own is not execution metadata.
`coverage_notes`	No	Tier/coverage caveats for the recommended tools (free-tier previews, depth gates, honest-unknown semantics)
`parallelizable`	No	true when at least one execution wave holds 2+ steps — the plan is not purely sequential
`estimated_calls`	No	Total estimated API calls for the whole plan (sum of per-step estimates)
`execution_waves`	No	The execution graph as concurrency waves: array of arrays of step numbers; every step in a wave can run concurrently once earlier waves finish (derived from depends_on)
`matched_classes`	No	Every intent class that scored, with its score — the router's full deterministic trace
`intent_confidence`	No	How confident the router is that it read the QUESTION right (0-1, deterministic) — driven by keyword score + margin over the runner-up class
`planner_rationale`	No	One sentence on why the PLAN has this shape (ordering / parallelism / what mints what) — distinct from reason, which covers intent routing
`execution_estimate`	No	r-planner-v3 deterministic cost preview: {estimated_calls (plan NODE count — one per step; the top-level estimated_calls is the fan-out-weighted API-call total), estimated_latency_ms (sum over waves of the SLOWEST tool in each wave, from a static 3-tier table: heavy synthesis 3000ms / standard read 1200ms / light free read 500ms), parallelizable (any wave holds 2+ steps)}
`execution_strategy`	No	r-planner-v3 explicit strategy: {parallel_groups: string[][] — execution_waves rendered as TOOL-NAME arrays (e.g. [["get_grid_intelligence","get_interconnection_queue","get_refined_queue"]]), note: plan-only disclaimer — this tool only plans; execute the sequence yourself}
`workflow_confidence`	No	How confident the router is that the plan can EXECUTE cleanly with the signals in hand (0-1, deterministic) — boosted by resolved context signals, docked for placeholder args the user must still supply; step-minted placeholders don't dock
`recommended_sequence`	No	Ordered tool sequence mirroring the DC Hub recipe for the matched intent class
`site_evaluation_handoff`	No	Pre-built follow-up calls (analyze_site / get_water_risk args) when the payload carries coordinates — an array of {tool, parameters, why} entries.
`workflow_confidence_basis`	No	The arithmetic behind workflow_confidence: {resolved_signals, minted_placeholders, user_supplied_placeholders}

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, but the description adds substantial context: deterministic keyword routing, no LLM, no network, same intent always returns the same plan, free, and 'never executes.' It also details the auditable replay structure and warns about args_hint substitution, going well beyond annotation basics. No contradiction 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 long but front-loaded with the critical 'INSPECT-ONLY' caveat and usage rule. Detailed return-structure enumerations are somewhat redundant with the output schema, but they support the audit/log/diff use case and are organized; no sentence is filler, though it could be tightened.

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 a complex planning tool with an output schema, the description covers all necessary decision points: what it does, what it does not do, when to use alternatives, determinism guarantees, output structure, and practical parameter hints. The pointer that tools/list stays canonical for schemas closes the loop on schema authority.

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 description coverage is 100%, so baseline is 3. The description adds useful operational meaning by explaining that context hints sharpen the plan and explicitly warning that <angle-bracket> args_hint values must be substituted from earlier steps, never invented. This extends beyond the plain parameter 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 opens with 'INSPECT-ONLY — returns the plan WITHOUT running it,' a specific verb and resource with an explicit non-execution boundary. It immediately distinguishes itself from execute_plan and discover_tools, so an agent can tell siblings apart without opening schemas.

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 states exactly when to use this tool ('only to review, log, diff or audit a plan before executing it yourself') and names the alternatives with conditions: execute_plan for real multi-step questions, discover_tools for family-level browse. This is explicit when/when-not guidance with no inference required.

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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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.

Plan Query

Input Schema

Output Schema

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