x402 crypto market structure

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 key behavioral traits: the tool returns historical data (implied read-only), specifies data granularity (1-hour bars), and mentions constraints (up to 7 years, max 5,000 bars per call). However, it lacks details on error handling, rate limits, authentication needs, or response format, which are important for a tool with data retrieval.

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 extremely concise and front-loaded, consisting of just two sentences that efficiently convey the tool's purpose and key constraints. Every word earns its place, with no redundant or vague language, making it easy for an AI agent to quickly understand the tool's functionality.

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 (historical data retrieval with two parameters) and lack of annotations and output schema, the description is moderately complete. It covers the data type, granularity, and constraints but omits details on response format, error conditions, and authentication requirements. For a tool without structured output documentation, more context on what the tool returns would be beneficial to achieve a higher score.

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 100% description coverage, with both parameters (token and limit) well-documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as explaining token symbol formats or bar count implications. Given the high schema coverage, a baseline score of 3 is appropriate as the description does not compensate but also does not detract.

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 specific verbs ('Hourly OHLCV price history with buy/sell flow data') and distinguishes it from siblings by specifying the time granularity ('1-hour bars'). It explicitly mentions the resource (price history data) and scope (up to 7 years, up to 5,000 bars per call), making it easy to differentiate from tools like history1d or history5m.

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 clear context for when to use this tool by specifying the data type (hourly OHLCV with buy/sell flow) and limitations (up to 7 years, max 5,000 bars). However, it does not explicitly state when not to use it or name alternatives (e.g., history1d for daily data or history5m for 5-minute data), which would be needed for a score of 5.

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