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startmining-mcp

startmining-mcp

by TIBSP
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

calculate_profitability

Estimate Bitcoin mining profitability from hashrate, power watts, and electricity cost. Input BTC price, pool fee, and days to see projected profit or loss.

Instructions

Calculate mining profitability for given hashrate and electricity cost

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "hashrate_th": "<hashrate_th>",
      "power_watts": "<power_watts>",
      "electricity_cost": "<electricity_cost>"
    },
    "name": "calculate_profitability"
  }
}

Input Schema

NameRequiredDescriptionDefault
daysNoNumber of days to calculate (default: 30)
btc_priceNoBTC price (optional, uses current if not provided)
hashrate_thYesHashrate in TH/s
power_wattsYesPower consumption in watts
pool_fee_pctNoPool fee percentage (default: 2)
electricity_costYesElectricity cost in $/kWh

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full disclosure burden. It never states that this is a read-only computation, which network assumptions it uses (current difficulty, block reward, hashprice), or that results are estimates rather than live network data — all material for a profitability model.

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?

A single compact sentence with no filler and the core resource front-loaded. It is under-specified rather than bloated, so length is not the problem.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With six parameters, no annotations, and no output schema, the description should say what the calculation returns (daily/monthly revenue, profit, breakeven days) and what network assumptions underpin it. Neither is present, so an agent cannot predict the result shape or trust boundary.

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 six parameters including defaults for days, pool_fee_pct, and btc_price. The description names only hashrate and electricity_cost and omits the required power_watts, so it adds nothing beyond the schema; baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Calculate mining profitability') with the two headline inputs, so an agent immediately knows the domain. It does not, however, distinguish itself from the closest sibling, calculate_breakeven, which also takes hashrate/cost inputs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no indication of when to pick this over calculate_breakeven or any of the data-retrieval siblings. No exclusions, prerequisites, or scenario cues are given, leaving the agent to infer selection from the name alone.

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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