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by BackpowCom

Mining hardware economics

get_hardware_benchmarks
Read-onlyIdempotent

Find profitable mining hardware by querying ASICs, GPUs, or CPUs, ranked by net daily profit at your electricity cost. Get hashrate, power draw, revenue, and coin matches to decide what to mine.

Instructions

Mining hardware matched to the coins it can mine, ranked by net USD per day at a given electricity rate. Covers ASICs, GPUs and CPUs, with hashrate and power draw per coin, gross revenue, net profit, and market price including whether that price is an observed listing or a model estimate. Use for "what should I mine with an RTX 4090", "best ASIC for Scrypt", "is an Antminer S21 still profitable", or "how much does a Bitaxe make". Coins are ranked by profitability, not alphabetically, and results are capped — ask for a higher limit rather than assuming the list is complete. For a coin's economics independent of any particular rig, use get_cost_of_production.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoRestrict to one hardware class.
limitNoDevices to return, 1-25. Defaults to 5.
queryYesHardware model or keyword, at least 2 characters, e.g. "RTX 4090", "Antminer S21", "Bitaxe", "Ryzen 9 7950X". Short or generic queries match hundreds of devices, so pair them with type and limit.
coin_idNoRestrict the per-device coin rows to one network, e.g. to answer "how much does this rig make on Kaspa". Optional.
electricity_cost_usd_kwhNoElectricity price in USD per kWh. Defaults to 0.069, an industrial hosting tariff; residential rates are commonly 3-5x higher and change profitability conclusions.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, covering the safety profile. The description adds behavioral context beyond that: results are ranked by profitability, results are capped (with a hint to request a higher limit), and market price may be an observed listing or a model estimate. These are useful traits not present in annotations. It doesn't add rate limits or error behavior, but with annotations covering safety, the extra detail is sufficient. No contradiction with 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 slightly long but every sentence adds value: first sentence states purpose and key features, second gives example queries, third explains ranking and capping, fourth routes to an alternative. It's front-loaded with the core purpose and well-structured. It's not overly verbose, though it could be trimmed slightly; still, the density is justified by the tool's complexity.

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 no output schema, the description lists the return fields (hashrate, power draw, gross revenue, net profit, market price, price type), explains ranking and capping, provides example queries, and gives an alternative tool for other use cases. With 5 parameters, this description covers all essential aspects an agent needs to call it correctly: what it does, when to use it, key behaviors, and alternatives. Nothing critical is missing.

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?

The input schema has 100% description coverage for all 5 parameters, so the schema already documents each parameter thoroughly (type enum, limit range and default, query min length, coin_id pattern, electricity cost default and impact). The tool description adds only marginal semantic value, e.g., mentioning that electricity rate affects profitability and hinting at the limit cap. Baseline for high coverage is 3, and the description doesn't significantly exceed that.

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 (get hardware benchmarks), and clearly defines what it does: matches mining hardware to coins, ranks by net USD/day, and covers ASICs/GPUs/CPUs. It explicitly distinguishes itself from get_cost_of_production, which is a sibling, by stating the alternative use case. The purpose is unambiguous and unique among siblings.

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 gives multiple concrete example queries ('what should I mine with an RTX 4090', 'best ASIC for Scrypt', etc.), explains the ranking basis (profitability, not alphabetical), warns about result capping and advises asking for a higher limit, and explicitly directs to get_cost_of_production when the user wants coin economics independent of hardware. This is explicit when-to-use and when-not-to-use guidance with an alternative named.

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