agent-data-pro

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

Since no annotations are provided, the description carries the full behavioral burden. It clearly discloses the paid nature (USDC amount, Base network, x402), the requirement to call list_articles first, and the type of content returned. It does not detail failure modes or response format, but covers the most critical operational aspects.

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 front-loaded with the core retrieval purpose and domain, then provides payment details and the prerequisite. It is slightly verbose (five short sentences), but every sentence conveys useful operational information, so it remains efficient.

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?

For a one-parameter, paid retrieval tool, the description covers the ID source, payment requirements, and expected content type. It lacks explicit error handling and response format details, but the 100% schema coverage and clear sibling context minimize ambiguity.

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%: the schema already documents the id parameter and even includes the list_articles hint. The description repeats that hint but adds no new semantic detail beyond the schema, aside from the indirect note that price varies by article. Baseline 3 is appropriate.

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 uses a specific verb ('Retrieve') and resource ('full content of a premium research article'), explicitly scoped to Blockchain, DeFi, and Crypto Markets. It also differentiates from siblings by instructing the agent to call list_articles first, making the tool's role in the workflow clear.

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 gives clear usage context: call list_articles first to get valid article IDs, and be aware of the x402 payment requirement. It does not explicitly list when not to use this tool versus get_crypto_price, but the domain scoping makes that distinction obvious.

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

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

No annotations are provided, so the description carries the full burden. It discloses the data source (cryptorates.ai), update cadence, and rate limit, making the fetch behavior transparent. It does not explicitly state it is read-only, but the nature of retrieving price data makes that obvious.

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, well-structured sentence that efficiently conveys purpose, supported coins, data source, update frequency, and rate limit. No redundant or extraneous information.

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?

For a simple one-parameter tool with no output schema, the description provides sufficient context: what it returns, how often data is updated, and usage limits. Nothing critical is missing for an agent to decide when and how to call it.

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 schema itself provides 100% coverage of the single 'symbol' parameter with a long list of examples. The description does not add additional semantic detail beyond the schema, so the baseline score of 3 is appropriate.

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 fetches current cryptocurrency price data and specifies the exact data points (USD price, 24h change, 7d change, market cap, 24h volume). It is unambiguous and distinct from the sibling article tools.

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?

While it doesn't explicitly state 'use when you need a current price', the purpose is so clear that usage is implied. It provides useful operational context including the 5000+ coin support, 5-minute update frequency, and rate limit of 30 requests per 60 seconds.

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

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

No annotations are provided, so the description carries the behavioral disclosure burden. It explains that the call is free ('no payment required') and that it returns article metadata with previews. For a read-only zero-parameter listing, this is adequate, though it does not mention pagination or response format.

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?

Three short sentences, each earning its place: purpose, cost/read-only nature, and usage guidance. The description is front-loaded and free of 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?

For a simple zero-parameter listing tool with no output schema, the description covers purpose, content, cost, and correct usage order. Minor omissions like sorting or pagination do not prevent an agent from using it effectively.

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?

Input schema has zero parameters and is fully covered by the schema itself, so no parameter explanation is needed. Baseline 4 applies for zero-parameter tools.

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?

Description states a specific verb ('List') and resource ('available premium research articles') and specifies the returned fields: prices, categories, and previews. It is clearly distinct from the siblings get_article and get_crypto_price.

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?

Explicitly instructs 'Use this first to discover available article IDs,' which tells an agent when this tool should be invoked. It does not explicitly contrast with get_article, but the usage guidance provides clear context.

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