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

With no annotations, the description carries the transparency burden. It discloses specific output details (e.g., 'download volume (estimated weekly from 90-day totals)') and the type of signals returned. It doesn't mention failure modes, authentication, or side effects, but for a read-only lookup tool, the provided behavioral details are robust and exceed minimal expectations.

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 and well-structured: a clear opening statement, a list of return signals, a motivational risk paragraph, and explicit use cases with example values. Every sentence adds value, and the front-loading ensures the agent immediately knows the tool's purpose.

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 or annotations, the description provides sufficient context: what it does, what it returns (detailed signal list), and when to use it. It even provides example crate names to guide parameter construction. The description is complete enough for an agent to select and invoke the tool correctly.

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 already provides full parameter documentation with examples and case-insensitivity note (100% coverage). The description reinforces the crate context but doesn't add new parameter-specific meaning beyond what the schema states. Per rubric, baseline is 3 when schema coverage is high.

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 starts with a specific verb and resource: 'Get a behavioral commitment profile for any Rust crate on crates.io.' It clearly distinguishes this from sibling lookup tools for npm, PyPI, and Go modules by focusing on crates.io and Rust-specific signals. The scope is unambiguous.

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 explicit use cases: 'vetting Rust dependencies before adding to Cargo.toml, identifying abandonware, supply chain risk assessment.' This gives clear context for when to use the tool, though it doesn't explicitly name alternatives or say when not to use it. Since the tool is language-specific, the use cases effectively differentiate it from sibling lookups.

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