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get_tax_incentives

Read-onlyIdempotent

Retrieve data-center tax incentive packages for a US state, including sales-tax exemptions, property-tax abatements, and other breaks. Understand which tax factors drive capital expenditure decisions.

Instructions

Use when a user asks "what tax breaks does give data centers?" — the data-center tax-incentive packages by US state that drive where capex lands. Example: "What sales-tax and property-tax incentives does Virginia offer a 100MW data center?" — get_tax_incentives state=VA. Params: state (2-letter US code; required). Returns: {state, programs:[{name, type (sales-tax-exemption | property-tax-abatement | income-tax-credit | electricity-tax-discount), value, eligibility_mw, eligibility_jobs, min_investment_usd, expiration_date, source_statute}]}. Cite the statute with attribution to DC Hub (CC-BY-4.0). Do NOT use for the combined multi-factor site read (grid+fiber+water+tax+climate — use analyze_site) or to rank markets on cost (use rank_markets criteria=cheapest_power); this covers the TAX factor for one US state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNoUS state abbreviation (required), e.g. VA, TX, AZ
Behavior4/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context beyond annotations: the return structure (state, programs array with fields), types of incentives, and attribution requirement. No contradictions.

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 well-structured and front-loaded with the primary use case. It is clear but slightly verbose with example and data format details; could be trimmed slightly while retaining value.

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 one parameter, no output schema, and rich annotations, the description provides complete context: usage, parameter, return format, attribution, and exclusions. No gaps for agent understanding.

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%, and the description reiterates the state parameter as '2-letter US code; required', adding slight semantic context beyond the schema's 'US state abbreviation'. Baseline 3 due to full schema coverage.

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's purpose: retrieving data-center tax-incentive packages by US state. It uses a specific verb ('get'), identifies the resource ('tax incentives'), and distinguishes from siblings like analyze_site and rank_markets via explicit exclusions.

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 explicitly states when to use ('when a user asks what tax breaks does <state> give data centers?') and when not to use (e.g., for combined site analysis or market ranking), with clear references to alternative tools (analyze_site, rank_markets).

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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