radmail_learning_insights

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

With no annotations, the description must carry the full burden of behavioral disclosure. It promises a read-only transparency feature but does not mention side effects, data access, permissions, or return behavior. The high-level phrase 'Transparency, not a black box' lacks specifics about what the tool does beyond showing insights.

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, using two sentences to convey the core functionality and a behavioral caveat. The first sentence is information-dense and front-loaded with the main purpose; the second sentence adds a brief value statement without excessive verbosity.

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 tool with no output schema and two optional parameters, the description offers a reasonable high-level overview but leaves gaps: it does not clarify what 'learned response shape' means, how results are structured, or what the agentId parameter affects. It is minimally adequate but not fully complete.

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 description does not explain either parameter (agentId or includeBacklog). Schema coverage is 50%, with only includeBacklog having a description, and the description adds no meaning beyond that. It fails to compensate for the undocumented agentId parameter or clarify the role of the backlog.

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 a specific verb ('Show') and resource ('what RadMail has learned about how YOU work'). It enumerates concrete types of learned information (most-used tools, response shape, recurring focus, capability wishlist), distinguishing it from sibling tools like triage or search, which address different actions.

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 implies usage as a transparency mechanism ('Transparency, not a black box'), suggesting it is meant for users who want to understand the system's model of them. However, it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites.

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