Skip to main content
Glama
azmartone67

DC Hub — Data Center & Energy Intelligence

M&A Transactions

list_transactions
Read-onlyIdempotent

Retrieve data-center M&A and capital deals filtered by date, value, region, buyer, or seller. Returns deal details including value, parties, date, market, and type (acquisition, JV, refinance, recap).

Instructions

M&A and capital transactions in the data center sector — 2,000+ 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. Answers "which data-center deals closed this year", "what was that acquisition worth". 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.
Install Server

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate readOnly, idempotent, non-destructive behavior, so the bar is lower. The description adds valuable behavioral context: the dataset covers 2,000+ deals from 2019-present, values are only included where public, many private deals are undisclosed, and the tool focuses on broad deal flow. This goes well beyond the annotations without contradicting them.

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 front-loaded with the core purpose, then moves through returned fields, filters, example use, and exclusions. Every sentence adds practical value for selecting and invoking the tool, and there is no wasted or redundant content.

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 10-parameter, filterable transaction-list tool with an output schema, the description covers the domain, return fields, filter options, data limitations, example usage, and sibling exclusions. Nothing critical is missing for an agent to select and invoke this tool correctly.

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 description coverage is 100%, so the schema already documents all 10 parameters thoroughly. The description adds a useful example (min_value_usd=1000000000) and clarifies the absence of a `year` parameter, but most parameter meaning is properly carried by the schema. Baseline 3 is appropriate.

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 clearly defines the tool as returning M&A and capital transactions in the data center sector, enumerates the returned fields, and explicitly distinguishes it from hyperscaler_deals and deal_autopsy. An agent can understand exactly what this tool does and how it differs from nearby sibling tools.

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 includes explicit when-to-use guidance with question examples, a concrete invocation example, and clear exclusions: 'do NOT use for hyperscaler-specific lease/PPA/JV activity (use hyperscaler_deals) or a single-deal post-mortem (use deal_autopsy).' It also warns that there is no `year` parameter, so the agent will not misuse filters.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/azmartone67/dchub-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server