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

With no annotations provided, the description carries the burden of behavioral disclosure. It reveals the output behavior well: 'Each row says yes/no/partial for seven real clients and explains what goes wrong.' This is meaningful beyond the tool name, though it does not describe filtering behavior in detail.

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 and front-loaded with the tool's identity as a support matrix. Each sentence adds distinct information: what it covers, how rows are structured, and when to use it. There is no redundant filler.

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 read-only lookup tool with one optional parameter and no output schema, the description gives sufficient context: supported topics, client count, row semantics, and the purpose. It does not specify the exact return data structure, but the 'each row says yes/no/partial' phrasing covers the essential output expectations.

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 documents the optional 'feature' filter with 100% coverage. The description adds value by enumerating concrete feature names an agent might query (tables, flex/grid, background-image, etc.), effectively expanding the known parameter vocabulary beyond the schema's two examples.

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 identifies the tool as a support matrix for HTML/CSS features affecting email signatures and states its use case: 'Use it to answer questions about email HTML support.' The explicit feature list and per-client yes/no/partial format distinguish it from sibling tools like check_email_signature or create_email_signature.

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 context for when to use the tool: to answer questions about email HTML support and client-specific rendering. It does not explicitly name alternatives or state when not to use it, but the sibling tool names make the intended role obvious.

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