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Glama

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

by cloud.dchub

Get Market Context

get_market_context
Read-onlyIdempotent

Use when an agent needs a WHOLE-market briefing it can drop straight into its context window — one call returns a token-budgeted context pack for a data-center market: DCPI verdict, power & grid facts, the Claude-written 12-month outlook, M&A deals, construction pipeline, operator footprint, transaction comps, risk factors, and top news — each section with its own token count, as_of timestamp, and citable URL, greedily filled in that priority order under your max_tokens budget. Example: "Brief me on the Columbus data-center market" — get_market_context market=columbus max_tokens=4000. Params: market (required, market slug e.g. northern-virginia — valid slugs come from rank_markets); max_tokens (optional, 200-8000, default 4000). Returns {sections:[{id,title,text,tokens,as_of,cite}], used_tokens, omitted}. Do NOT use for a single metric (use get_market_dcpi_rank), the raw structured metric set (use get_market_intel), or cross-market ranking (use rank_markets); this is the narrative briefing pack. Cite "DC Hub (dchub.cloud)".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketYesMarket slug (required), e.g. northern-virginia, dallas, phoenix — valid slugs come from rank_markets / get_market_dcpi_rank
max_tokensNoToken budget for the pack, 200-8000 (default 4000); sections are filled in priority order until the budget is spent

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?

The description goes well beyond the readOnly/idempotent/destructive annotations by explaining the greedy priority-order filling under a max_tokens budget, per-section token counts and as_of timestamps, citable URLs, and the omitted field in the return object. It even discloses that the outlook is Claude-written, which is useful context an agent could not infer from 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 information-dense, with purpose front-loaded, then an example, parameter semantics, return shape, and explicit exclusions. Every sentence adds value, though the density could be slightly streamlined for faster scanning.

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?

The description covers what the tool returns, how the budget works, where valid slugs come from, which sibling tools to use instead, and the required citation string. Given the tool's complexity and the rich output schema information embedded in the description, nothing critical is missing for correct invocation.

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 baseline is 3. The description adds real meaning beyond the schema: it explains that market is a slug sourced from rank_markets, that max_tokens controls greedy section filling in priority order, and provides a concrete invocation example. This is meaningful enrichment rather than mere duplication.

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 states a specific verb and resource: returns a whole-market briefing pack for a data-center market, enumerating the exact sections included and the token-budgeting behavior. It actively distinguishes itself from siblings by naming get_market_dcpi_rank, get_market_intel, and rank_markets as alternatives for narrower or different needs.

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 opens with 'Use when an agent needs a WHOLE-market briefing' and then gives explicit negative guidance: do not use for a single metric, raw structured metrics, or cross-market ranking, with specific sibling tools named for each case. This gives an agent unambiguous routing criteria.

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

A3.8/5.0
Disambiguation1/5

With 85 tools, several families are heavily overlapping: semantic_search and search_intelligence are explicitly documented as the same retrieval with different call shapes, save_site and save_to_shortlist both persist sites, and list_saved_sites and get_shortlist both read saved sites. Additionally, site scoring is split across analyze_site, get_composite_site_score, score_facility, and rank_sites, making correct tool selection very difficult for an agent.

Naming Consistency3/5

All names are snake_case and mostly readable, but conventions are mixed: many use get_* (get_facility, get_grid_intelligence), others use verb phrases (analyze_site, compare_isos, rank_markets), and some are bare noun phrases (ai_capacity_index, hyperscaler_deals, grid_transition_radar, site_selection_canvas). The search family alone uses search, search_facilities, semantic_search, and search_intelligence with no consistent pattern.

Tool Count1/5

85 tools is an extreme count for any MCP server, far beyond the 3-15 well-scoped range and above the 50+ threshold described as an extreme mismatch. Even with a wide domain like data-center siting, this many tools overwhelms agent context and makes selection costly.

Completeness4/5

The domain surface is exceptionally broad: siting, grid, gas, fiber, water, climate, tax, permitting, deals, news, facilities, saved shortlists, alerts, webhooks, key management, and research dossiers are all covered with connected workflows. Minor gaps exist, such as no delete or update for saved sites and no pause/resume for standing intents, but these are workable rather than blocking.

Resources