Bitcoin-MCP-Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it returns exactly 7 items ('top 7'), includes specific metrics (e.g., floor price, trading volume), and explains the data source ('CoinGecko', 'real user search behavior'). However, it doesn't mention potential limitations like rate limits, authentication needs, or data freshness, leaving some 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: the first sentence states the core purpose, followed by a bulleted list of return metrics and explicit usage guidelines. Every sentence adds value, though it could be slightly more concise by integrating the 'Use cases' section more tightly with the opening statement.

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 simplicity (0 parameters, output schema exists), the description is complete. It covers purpose, usage, output details, and data source. The output schema likely handles the return structure, so the description doesn't need to explain formatting. It provides all necessary context for an agent to understand when and how to use this tool effectively.

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, detailing the 7 metrics returned. It adds value by explaining terms like 'floor price' and clarifying the ranking system ('1-7 based on search popularity'), which goes beyond what a basic output schema might provide.

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: 'get the top 7 trending NFT collections sorted by the most popular user searches on CoinGecko.' It specifies the verb ('get'), resource ('trending NFT collections'), scope ('top 7'), and sorting criteria ('most popular user searches'), distinguishing it from sibling tools that focus on Bitcoin, mining pools, or other cryptocurrency data.

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 to discover which NFT collections are currently attracting the most attention, identify trending digital art or collectibles, or monitor NFT market activity.' It also clarifies the data source ('based on real user search behavior'), helping differentiate it from alternatives like get_trending_coins or get_trending_categories.

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