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 by specifying the return format (detailed metrics in string format for 6 categories), the data included (category name, rank, coins count, market cap, 24h change, trading volume), and the data source (CoinGecko user searches). However, it doesn't mention potential limitations like rate limits, freshness of data, or error handling, leaving some behavioral aspects uncovered.

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, starting with the core purpose, followed by return details, category explanation, and use cases. Every sentence adds value without redundancy, and it efficiently conveys necessary information in a compact format, making it easy for an AI agent to parse and understand.

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 complexity (no parameters, but rich output details), the description is complete enough. It thoroughly explains what the tool returns, the context of categories, and when to use it. With no annotations and an output schema not provided in the context signals, the description compensates by detailing the output format and metrics, ensuring the agent has sufficient information to invoke and interpret results correctly.

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 tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately adds no parameter information since none are needed, and it doesn't waste space on irrelevant details. It focuses instead on the output semantics, which is valuable given the absence of an output schema in the provided context.

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 with a specific verb ('get') and resource ('top 6 trending cryptocurrency categories'), distinguishing it from siblings like 'get_trending_coins' or 'get_trending_nfts' by focusing on categories rather than individual coins or NFTs. It explicitly mentions the data source (CoinGecko) and the sorting criteria (most popular user searches).

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 guidelines by listing specific use cases: 'identify which cryptocurrency sectors are gaining attention', 'understand macro trends in the crypto ecosystem', and 'find emerging narratives'. It also clarifies the data source ('based on real user search behavior for category pages'), which helps differentiate it from sibling tools that might use different metrics or data sources.

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