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bjunjo

treasury-mcp

by bjunjo

get_fee_recommendation

Provides Bitcoin network fee recommendations using current estimates and raw rate data to optimize transaction costs.

Instructions

Get a plain-English fee recommendation based on current estimates, with raw rate data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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 burden of behavioral disclosure. It states the tool provides a 'fee recommendation' and includes 'raw rate data', but doesn't describe key behaviors: whether it's read-only, requires network calls, has rate limits, or how it generates recommendations. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational traits.

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?

The description is a single, efficient sentence that front-loads the core purpose ('Get a plain-English fee recommendation') and adds clarifying details ('based on current estimates, with raw rate data'). Every word earns its place, with no redundancy or unnecessary elaboration, making it highly concise and well-structured.

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

Completeness3/5

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

Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description is minimally adequate. It explains the output ('plain-English fee recommendation' with 'raw rate data'), which aligns with the output schema's role. However, with no annotations and multiple sibling tools (e.g., 'get_fee_estimates'), it lacks context on differentiation and behavioral details, leaving room for improvement.

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

Parameters4/5

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

The tool has 0 parameters, and schema description coverage is 100% (though trivial due to no parameters). The description doesn't need to explain parameters, and it appropriately doesn't mention any. It focuses on output characteristics ('plain-English fee recommendation' with 'raw rate data'), which is sufficient given the parameterless design.

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?

The description clearly states the tool's purpose: 'Get a plain-English fee recommendation based on current estimates, with raw rate data.' It specifies the verb ('Get'), resource ('fee recommendation'), and output characteristics ('plain-English' with 'raw rate data'). However, it doesn't explicitly differentiate from sibling tools like 'get_fee_estimates' or 'estimate_smart_fee', which likely provide more technical or raw fee data.

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?

The description provides no guidance on when to use this tool versus alternatives. It mentions 'based on current estimates' but doesn't specify context (e.g., for transaction planning, analysis) or compare to siblings like 'get_fee_estimates' or 'estimate_transaction_cost'. Without explicit when/when-not instructions, the agent lacks clear usage direction.

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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