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get_pipeline

Read-only

Retrieve announced, permitted, or under construction data center projects. Filter by market, operator, country, capacity, and expected completion date.

Instructions

Use when a user asks "what is being built / announced / permitted" in a market or by an operator — the forward-looking construction pipeline (540+ projects, 369 GW). Example: "What data centers are under construction in Northern Virginia and when do they come online?" — get_pipeline market=northern-virginia status=construction. Params: status one of "announced" | "permitted" | "construction" | "operational"; operator (e.g. "Equinix", "Digital Realty", "AWS"); country (ISO-2, e.g. "US", "DE"); min_capacity_mw (e.g. 50 to filter hyperscale); expected_completion_before (ISO date, e.g. "2027-01-01"); limit/offset for pagination. Returns: {projects:[{name, operator, capacity_mw, status, expected_commissioning, market_slug, country, lat, lon}], total, generated_at}. Do NOT use for already-operational facilities (use search_facilities) or for the M&A deal flow (use list_transactions).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
offsetNo
statusNo
countryNo
operatorNo
min_capacity_mwNo
expected_completion_beforeNo
Behavior4/5

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

Annotations declare readOnlyHint=true; description adds context about scale (540+ projects), return structure, and pagination, but no additional behavioral traits beyond the read-only nature.

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 relatively long but efficiently packed with purpose, usage, parameter details, and return format. Minor redundancy in parameter list could be tightened, but overall well-structured.

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 tool with 7 parameters, no output schema, and complex domain, the description covers purpose, examples, all parameter semantics, return structure, and forbidden usage, making it fully self-contained.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description fully documents each parameter with types, examples, and constraints (status enum, ISO-2 country, etc.), compensating completely for lack of schema descriptions.

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 states the tool retrieves forward-looking construction pipeline data (540+ projects, 369 GW) for queries about what is being built/announced/permitted. It distinguishes from siblings like search_facilities and list_transactions.

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?

Explicitly provides when-to-use with examples, and specifies when not-to-use (operational facilities use search_facilities, M&A deals use list_transactions), naming alternatives.

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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