predict_fees

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

The description mentions the cost (50 sats per call) but does not clarify authentication requirements or what happens if no preimage/macaroon is provided. Since no annotations exist, the description carries the full burden but only provides minimal behavioral context.

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 a single sentence with no filler, front-loading the core purpose and cost. Every part adds value.

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?

The description lacks information on authentication setup (API key vs L402), expected confidence range, and data format of predictions. For a prediction tool with no output schema, more detail on return values would be beneficial.

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 coverage is 100%, and the descriptions for preimage and macaroon are already clear in the schema. The tool description adds the cost context ('50 sats per call') which is not parameter-specific, so it does not significantly enhance parameter meaning beyond the schema.

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 tool's purpose is clearly stated as 'EMA-based fee prediction with confidence + direction (rising/falling)'. It specifies the method (EMA), the output (confidence and direction), and distinguishes from siblings like get_fees (current fees) and fee_quote (quotes).

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?

No guidance on when to use this tool versus alternatives such as get_fees or fee_quote. The description implies use for prediction but does not provide context on prerequisites or when not to use it.

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