Bitcoin-MCP-Server

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

No annotations are provided, so the description carries the full burden. It effectively discloses key behavioral traits: the tool returns fee recommendations in a specific string format with four priority levels, includes dynamic fee changes based on network congestion, and details what each recommendation contains (fee rate, confirmation time, total cost). However, it lacks explicit mention of rate limits, error handling, or data freshness, which are minor gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded: it starts with the core purpose, then details the return format with bullet points, explains dynamic behavior, and ends with use cases. Every sentence adds value without redundancy, making it efficient and easy to parse.

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

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

Given the tool's low complexity (0 parameters, no annotations, but with an output schema), the description is complete. It thoroughly explains what the tool does, how to use it, and what to expect in the output, covering all necessary context for an AI agent to invoke it correctly without needing to rely heavily on the output schema.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on output semantics, explaining the return format in detail (four priority levels with specific attributes). This adds significant value beyond the structured output schema, though it doesn't fully compensate for the lack of output schema details (e.g., exact data types).

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

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

The description explicitly states the tool's purpose: 'get current recommended Bitcoin transaction fees for different confirmation speed priorities.' It uses specific verbs ('get', 'returns') and clearly identifies the resource (Bitcoin transaction fees), distinguishing it from sibling tools that focus on blocks, addresses, market data, or mining pools.

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

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

The description provides explicit usage guidance with a dedicated 'Use cases' section: 'When you need to know how much to pay for a Bitcoin transaction, to optimize transaction costs vs. speed, or to check if the network is congested.' This clearly indicates when to use this tool versus alternatives like market data or block information tools.

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