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Glama

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

by cloud.dchub

Search Intelligence

search_intelligence
Read-onlyIdempotent

Semantic (meaning-based) search over DC Hub's live intelligence corpus — industry news, M&A deals, discovered facilities and per-market DCPI analysis narratives — returning the most relevant records with citable source fields. This is the agent-friendly alias over the SAME retrieval layer as semantic_search: same results, different call shape. It takes query plus human-readable corpus names (news | deals | facilities | market_narratives); semantic_search takes q plus the raw table names. Call ONE of them, not both. Params: query (required, natural language); corpus (optional CSV of the four names above, default all); limit (1-15, default 8). BEHAVIOUR: read-only — it writes nothing, and repeat calls with the same arguments return the same records. ACCESS: works with no key, but anonymous results come back as a TRIMMED PREVIEW; the session X-API-Key hydrates full depth per key, and the free tier is capped per day (call claim_free_key once — no email — if you do not hold a key). Do NOT use when you can filter exactly: search_facilities for structured facility filters, get_news for date/keyword news, list_transactions for deal filters — those match fields and return complete sets, where this ranks by meaning and returns a top-N.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for query
limitNoMax results to return, 1-15 (default 8)
queryNoNatural-language query (required), e.g. "grids opening up for AI load in the Southeast"
corpusNoOptional corpus to restrict to: news | deals | facilities | market_narratives. CSV of several is allowed; default searches 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. 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?

While annotations already declare readOnlyHint and idempotentHint, the description adds substantive behavioral context: read-only writes nothing, repeat calls return same records, anonymous results are TRIMMED PREVIEW, session X-API-Key hydrates full depth, and free tier is capped per day. It also reveals the key/authentication requirement nuance and points to claim_free_key and unlock_more_data, far exceeding the annotation baseline.

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 dense but well-organized with labeled sections (Params, BEHAVIOUR, ACCESS, and the exclusion note). Every sentence carries functional weight; however, it is fairly long and partially repeats schema content, so it is not as lean as the highest tier of descriptions.

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 output schema exists and annotations cover safety/hints, the description supplies all remaining essentials: corpus options, parameter semantics, behavior, access limitations, rate caps, and clear routing to alternatives. An agent has everything needed to decide whether and how to invoke this tool.

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%, so the schema already documents each parameter. The description adds meaningful usage nuance: query is natural language with a concrete example, corpus can be a CSV of human-readable names, limit is bounded 1-15, and q is explicitly an alias. This reinforces and slightly extends the schema, though much is redundant with it.

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 resource: semantic search over DC Hub's live intelligence corpus, naming the corpora (industry news, M&A deals, discovered facilities, DCPI narratives) and the outcome (most relevant records with citable source fields). It also distinguishes itself from semantic_search by calling out the alias relationship and different parameter shape, so an agent can tell them apart without inspecting 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?

The description gives explicit when-to-use guidance: 'Do NOT use when you can filter exactly' and names the exact sibling tools (search_facilities, list_transactions, get_news) as alternatives for structured filters. It also warns 'Call ONE of them, not both' regarding semantic_search, leaving no ambiguity about 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 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.

Resources