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 discloses that the tool returns 'detailed metrics in string format' and lists specific data points (e.g., pool name, hashrate, addresses), which adds behavioral context. However, it doesn't mention error handling (e.g., what happens if the slug is invalid), rate limits, or authentication needs, leaving some gaps for a tool with no annotation coverage.

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 appropriately sized. It front-loads the core purpose, lists return data, explains the slug parameter with examples, and ends with use cases. While efficient, the bulleted list of return metrics is slightly verbose but earns its place by clarifying output content.

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 1 parameter with no schema descriptions, an output schema exists (so return values needn't be detailed), and no annotations, the description does well: it explains the parameter, outlines output data, and provides usage guidance. However, it lacks some behavioral details like error handling or rate limits, keeping it from a perfect score.

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% description coverage (no schema descriptions), so the description must compensate fully. It defines the 'slug' parameter as 'a unique identifier for each mining pool' and provides examples like 'foundry-usa' and 'antpool,' adding crucial semantic meaning beyond the bare schema. This fully addresses the parameter's purpose and format.

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 clearly states the tool's purpose as 'get comprehensive information about a specific Bitcoin mining pool using its unique slug identifier.' It specifies the verb ('get'), resource ('Bitcoin mining pool'), and key identifier ('slug'), distinguishing it from sibling tools like get_top_10_mining_pools_rank (which lists multiple pools) and get_mining_pools_hashrates_3month (which focuses on hashrates over time).

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 explicitly states when to use this tool: 'When you need detailed information about a specific mining pool that you already know by name or slug.' It also provides alternatives: 'You can obtain slugs from get_top1_mining_pool or get_top_10_mining_pools_rank,' guiding users to sibling tools for slug discovery before using this one.

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