check_arm64_deps

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 explains return structure, defines severity levels (error/warning/info) with concrete meanings, and gives a realistic example of outputs for cupy, tensorflow, and numpy. This goes well beyond a bare 'scan' statement.

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 longer than average but every section earns its place: purpose, use case, arguments, return format, severity semantics, and example. It is well-structured and front-loaded with the core purpose before any detail.

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 there is no output schema and no annotations, the description provides a complete picture: what inputs are needed, what the return object looks like, how issues are categorized, and a concrete example. An agent has enough information to invoke the tool correctly and interpret results.

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 description coverage is 0%, so the description must compensate for missing parameter documentation. It fully explains both parameters: file_content as raw manifest text, and file_type with exact allowed values ('requirements.txt', 'pyproject.toml', or 'Dockerfile'). It also demonstrates the call signature with an example.

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 opening sentence names a specific action and resource: scanning a dependency manifest for arm64 compatibility issues. It also clarifies the subject (Python dependencies) and target hardware (Arm64 servers like AWS Graviton), making it easy to distinguish from the unrelated sibling 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?

The description explicitly states when to use the tool: when a user wants to know whether Python dependencies install on Arm64 or why a package fails on arm64. It provides clear context, but it does not mention exclusions or alternative tools, so it stops short of a full when-not/alternative guide.

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