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azmartone67

DC Hub — Data Center & Energy Intelligence

M&A Transactions

list_transactions
Read-onlyIdempotent

Filter and retrieve data center M&A and capital transactions by date range, deal value, region, buyer, seller, or deal type.

Instructions

M&A and capital transactions in the data center sector — 1,800+ tracked deals (2019-present), each with its disclosed value where public (many private deals are undisclosed). Returns deal name, buyer, seller, value, date, market, target operator, type (acquisition/JV/refinance/recap). Filter by date range (date_from/date_to, ISO-8601), min_value_usd, region, buyer, or seller. Try: list_transactions date_from=2026-01-01 min_value_usd=1000000000. There is no year parameter — use date_from/date_to. Broad M&A and capital-deal flow with filters; do NOT use for hyperscaler-specific lease/PPA/JV activity (use hyperscaler_deals) or a single-deal post-mortem (use deal_autopsy).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
buyerNoFilter by acquiring company name, e.g. Blackstone, KKR, Digital Realty
limitNoMax results to return (1-500; default varies by tool)
offsetNoPagination offset, 0-based (skip this many results)
regionNoGeographic region filter, e.g. us, eu, apac, americas
sellerNoFilter by selling/target company name, e.g. CyrusOne
date_toNoLatest deal date, ISO-8601 (YYYY-MM-DD)
date_fromNoEarliest deal date, ISO-8601 (YYYY-MM-DD)
deal_typeNoDeal type filter, e.g. acquisition, jv, refinance, recap
max_value_usdNoMaximum disclosed deal value in US dollars
min_value_usdNoMinimum disclosed deal value in US dollars, e.g. 1000000000 for $1B+

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNoServing note
tierNoTier the response was served at
countNoRows returned in THIS response
quotaNoCaller quota state (remaining calls, tier) when available.
cachedNoWhether the response was served from cache
_entityNoPayload class discriminator (e.g. facility|market|iso_grid|queue_results|deal|report|response) — branch on this before parsing the rest.
successNotrue when the deal query succeeded
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.
data_sourceNoWhere the deal set comes from
total_countNoTotal deals matching the filter
total_valueNoAggregate disclosed value across the returned set (null when not computed)
_return_loopNoSuggested next-session delta call (get_changes since=24h) so you pull only what changed.
transactionsNoM&A / capital-transaction rows
total_value_unitNoUnit of total_value
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.
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable context beyond annotations: data coverage (1,800+ deals since 2019), the caveat that many private deals are undisclosed, the returned fields, and a note that there is no 'year' parameter. This gives the agent a realistic picture of data limitations without contradicting the annotations.

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 concise but information-dense. It leads with the core purpose, then return fields, filters, an example, and finally exclusion criteria. Every sentence earns its place; no redundancy or fluff.

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?

Despite having 10 parameters and an output schema, the description covers all essential context: what data is included, how to filter, example usage, and when NOT to use the tool (with sibling pointers). The presence of an output schema means return format doesn't need explanation, and the description fills all other gaps. It is highly complete for a tool of this complexity.

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% with rich descriptions for each of the 10 parameters. The description adds an example filter combination (date_from=2026-01-01 min_value_usd=1000000000), clarifies the absence of a 'year' parameter, and explains that value is disclosed only where public. This goes beyond the schema and helps the agent construct meaningful queries.

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 ('list') and resource ('M&A and capital transactions in the data center sector'), and immediately differentiates from siblings by naming hyperscaler_deals and deal_autopsy as alternatives. It also lists return fields, making the scope unambiguous.

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?

Explicit usage guidance is provided: 'Do NOT use for hyperscaler-specific lease/PPA/JV activity (use hyperscaler_deals) or a single-deal post-mortem (use deal_autopsy)'. It also includes a concrete example query and clarifies the date-range filter, making when-to-use very clear.

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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