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btc-miner-econ

Analyze Bitcoin mining economics and fee-market dynamics. Get current fee rates, miner revenue split, difficulty adjustment, pool concentration, and mempool backlog to time transactions and evaluate network health.

Instructions

Bitcoin mining economics and fee-market game theory via mempool.space. Returns current fee rates and pressure tier, miner revenue split (subsidy vs fees), next difficulty adjustment (direction, magnitude, blocks remaining), mining pool concentration (top-3 hashrate share), and mempool backlog size. Useful for agents reasoning about BTC transaction timing, miner incentive structures, or on-chain network health.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
includeNoSubset of data to return. 'all' returns every section (default). 'fees' = fee market only. 'difficulty' = next adjustment info only. 'pools' = pool hashrate distribution only. 'mempool' = backlog size only.
Behavior2/5

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

With no annotations, the description carries full burden. It does not disclose behavioral traits like read-only nature, safety, or side effects. Lists returned data but omits important context such as no destructive actions.

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?

Single well-organized paragraph with front-loaded purpose, enumerated data items, and closing use-case guidance. No wasted sentences.

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

Completeness4/5

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

Given the complexity of multiple data sections and no output schema, the description adequately explains all returned fields. Covers key aspects for a query tool, though could mention return format details.

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 coverage is 100% with parameter description. The tool description adds context about data sections but does not enhance parameter meaning beyond the schema. Baseline 3 applies.

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 it returns Bitcoin mining economics and fee-market game theory data, listing specific data sections. It distinguishes from sibling tools like 'btc-game-theory' by focusing on mining economics.

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

Usage Guidelines4/5

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

Provides context on when to use: 'useful for agents reasoning about BTC transaction timing, miner incentive structures, or on-chain network health.' Does not explicitly exclude alternatives but implies appropriate scenarios.

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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