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DC Hub Intelligence

Semantic Search

semantic_search
Read-onlyIdempotent

Search DC Hub's data-center and energy intelligence by meaning when keyword filters fall short, surfacing ranked news, deals, facilities, and market narratives.

Instructions

Use for CONCEPTUAL / fuzzy questions where keyword filters fall short — semantic (meaning-based) retrieval across DC Hub's industry news, M&A deals, the global facility map, per-market DCPI deep-dive narratives, press releases, announcements, permitting intel, construction permits, tax incentives and the capacity pipeline, ranked by relevance with citable source fields (news url/title, deal parties/value, facility name/location, deep-dive market/url). Examples: "what is happening with behind-the-meter gas for AI data centers?", "deals involving nuclear power for hyperscalers", "why is Northern Virginia constrained?" — semantic_search q="behind-the-meter gas for AI data centers". Params: q (required, natural-language query); corpus (optional CSV subset of news_articles,deals,facilities,market_narratives,press_releases,announcements,permitting_intel,construction_permits,tax_incentives,capacity_pipeline; default all); k (1-15, default 8). Returns {results:[{source_table, kind, text, score, cite:{…}}]}; score is 0-1 relevance, not a DCPI score. Complements the exact-filter tools (get_news / list_transactions / search_facilities) with relevance ranking; for a full token-budgeted market briefing use get_market_context. Cite "DC Hub (dchub.cloud)".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNoNumber of results, 1-15 (default 8)
qNoNatural-language query (required), e.g. "grids opening up for AI load in the Southeast"
queryNoAlias for q — the same natural-language query; send exactly one of q/query
corpusNoOptional CSV of corpora: news_articles, deals, discovered_facilities, market_narratives (default: all)

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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • removedInput schema / $schema
      Removed value: -"http://json-schema.org/draft-07/schema#"
    • removedOutput schema / $schema
      Removed value: -"http://json-schema.org/draft-07/schema#"
  2. Changed5 schema fields changedv2.3.20
    • removedOutput schema / properties / citation / additionalProperties
      Removed value: -{}
    • addedOutput schema / properties / citation / anyOf
      Added value: +[
      +  {
      +    "additionalProperties": {},
      +    "properties": {},
      +    "type": "object"
      +  },
      +  {
      +    "type": "string"
      +  }
      +]
    • changedOutput schema / properties / citation / description
      Previous value: -"Machine-readable citation: how to attribute DC Hub (dchub.cloud) for this payload."New value: +"Machine-readable citation: how to attribute DC Hub (dchub.cloud) for this payload. Normally an OBJECT {source, url, license, cite_as, retrieved_at}; a bare string is accepted and carries the attribution line itself."
    • removedOutput schema / properties / citation / properties
      Removed value: -{}
    • removedOutput schema / properties / citation / type
      Removed value: -"object"
  3. Changed1 schema field changedv2.3.14
    • changedInput schema / properties / query / description
      Previous value: -"Alias for q"New value: +"Alias for q — the same natural-language query; send exactly one of q/query"
  4. Changed1 schema field changedv2.3.12
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "$schema": "http://json-schema.org/draft-07/schema#",
      +  "additionalProperties": {},
      +  "description": "DC Hub envelope: structuredContent mirrors the JSON payload in content[0].text — tool-specific data fields ride at the top level alongside these envelope keys.",
      +  "properties": {
      +    "_entity": {
      +      "description": "Payload class discriminator (e.g. facility|market|iso_grid|queue_results|deal|report|response) — branch on this before parsing the rest.",
      +      "type": "string"
      +    },
      +    "_front_door": {
      +      "additionalProperties": {},
      +      "description": "In-band front-door hint (first workflow-entry tool of a session): call plan_query(intent) first for the ordered multi-step plan.",
      +      "properties": {},
      +      "type": "object"
      +    },
      +    "_return_loop": {
      +      "additionalProperties": {},
      +      "description": "Suggested next-session delta call (get_changes since=24h) so you pull only what changed.",
      +      "properties": {},
      +      "type": "object"
      +    },
      +    "citation": {
      +      "additionalProperties": {},
      +      "description": "Machine-readable citation: how to attribute DC Hub (dchub.cloud) for this payload.",
      +      "properties": {},
      +      "type": "object"
      +    },
      +    "provenance": {
      +      "additionalProperties": {},
      +      "description": "Collection-level provenance block: {source, method, as_of, verification_counts, cite_url_template, license, cite_as}. Quote the verification level when citing.",
      +      "properties": {},
      +      "type": "object"
      +    },
      +    "quota": {
      +      "additionalProperties": {},
      +      "description": "Caller quota state (remaining calls, tier) when available.",
      +      "properties": {},
      +      "type": "object"
      +    },
      +    "site_evaluation_handoff": {
      +      "anyOf": [
      +        {
      +          "items": {
      +            "additionalProperties": {},
      +            "properties": {},
      +            "type": "object"
      +          },
      +          "type": "array"
      +        },
      +        {
      +          "additionalProperties": {},
      +          "properties": {},
      +          "type": "object"
      +        }
      +      ],
      +      "description": "Pre-built follow-up calls (analyze_site / get_water_risk args) when the payload carries coordinates — an array of {tool, parameters, why} entries."
      +    }
      +  },
      +  "type": "object"
      +}
  5. Addedv2.3.7

TDQS

A4.4/5.0
Behavior4/5

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

It discloses that results are 'ranked by relevance' and that 'score is 0-1 relevance, not a DCPI score', which clarifies a potentially confusing output field. It also states the return shape and citable source fields. It does not mention rate limits or latency, but the annotations already cover read-only/idempotent safety, so this is a lower bar.

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 with when-to-use, followed by corpus list, examples, parameter summary, and return shape. It is dense but well organized. The long list of corpus types could be trimmed since the schema enumerates them, but it doesn't become confusing.

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 read-only search tool with a full output schema and rich annotations, the description covers when to use it, what it searches, how results are ranked, and the citation requirement. It is complete enough for an agent to invoke it correctly without further context.

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 coverage is 100%, so the schema already documents all four parameters (q, query, k, corpus). The description adds only the default value for corpus ('default all') and k range (1-15), which is already in the schema. It adds no syntax or usage details beyond what the schema provides, so baseline 3 applies.

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 a specific verb and scope: 'Use for CONCEPTUAL / fuzzy questions' and 'semantic (meaning-based) retrieval', which immediately distinguishes it from exact-filter tools. It enumerates the corpus types (news, M&A deals, facility map, DCPI narratives, permits, etc.) and names the sibling filter tools it complements.

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 explicitly states when to use this tool ('where keyword filters fall short'), provides concrete example questions (behind-the-meter gas, nuclear deals, Northern Virginia constraints), and names alternatives: get_news / list_transactions / search_facilities for exact filters, and get_market_context for a full briefing.

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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