get_market_briefing

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

No annotations exist, so the description carries full burden. It explains the tool's behavior: returns a natural-language summary with two verbosity levels and typical line counts. It does not discuss data freshness, latency, or error handling, but the behavior is straightforward and well-described.

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 concise (4 sentences) and front-loaded with the core purpose. Every sentence adds value, clearly explaining the tool's function and modes without redundancy.

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 (one optional parameter) and lack of output schema, the description covers the main aspects: data sources, modes, output length. It could mention error cases or data freshness, but is complete enough for effective use.

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?

Schema description covers 100% of the parameter, but the tool description adds meaning by detailing what each verbosity value yields ('brief returns roughly 4 lines...', 'detailed adds per-venue breakdown...'). This enriches the schema's enum descriptions.

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 a concise natural-language summary of the current Bitcoin market state.' It specifies the data sources it synthesizes and distinguishes between two modes (brief and detailed). The sibling tools are individual raw data calls, so this tool is well-differentiated.

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 advises when to use this tool: 'the most token-efficient way for an agent to get the "what's happening right now" picture — replaces 4-5 raw data calls plus the reasoning to summarise them.' This provides clear guidance against alternatives.

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