predict_btc_price

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

With no annotations, the description carries the burden and discloses key behaviors: it uses an LSTM model, has explicit pricing per prediction type, and states payments are handled automatically via x402. This adds context beyond the schema, though it stops short of explaining failure modes or output format specifics.

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 compact at four sentences, each providing valuable information: purpose, model, pricing, and payment method. It is front-loaded and free of filler, though the pricing list could be formatted as bullets for slightly better scanability.

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 schema already explains parameter values and output types, the description adequately covers the essential context: model, pricing, and payment. It lacks details on response structure or typical latency, but the tool is relatively simple, and the schema fills the gap for return semantics.

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% with clear parameter descriptions, but the description enriches the 'type' parameter by mapping each enum value to its price. This adds practical cost semantics that are not in the schema, helping the agent understand trade-offs between options.

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 function: 'Get AI prediction for BTC price.' It specifies the resource (BTC price) and the verb (Get), and differentiates from sibling tools by explicitly targeting BTC, unlike generic ones like predict_crypto_price or predict_direction. The mention of the LSTM model adds specificity.

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 implies usage for BTC price predictions and provides pricing tiers that help choose the prediction type, but it does not explicitly compare against sibling alternatives or state when not to use this tool. No exclusions or alternative tool references are given.

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