auth_recommend

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

With no annotations provided, the description fully carries the burden of behavioral disclosure. It clearly states that values never leave the vault, that it uses MCP sampling, that it sends only names and descriptions to the LLM, and that it degrades gracefully to returning the full list. This is comprehensive for a read-only recommendation tool.

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 well-structured with a summary, mechanism, fallback behavior, and Args/Returns sections. It is slightly longer than absolutely necessary but every sentence adds value, and the structure makes it easy to scan.

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 one parameter and a non-trivial output shape, the description fully covers the return fields (recommendation, reasoning, available), the security model, and the graceful degradation path. It is complete given the complexity and lack of annotations.

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 single parameter 'task' has no schema description (0% coverage), so the tool description must compensate. It does so effectively with 'What the credential will be used for' and a concrete example, adding meaning beyond the bare schema definition.

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 opens with a clear, specific statement: 'Ask goose's AI to pick the best stored credential for a task.' This distinguishes it from sibling tools like auth_get (which retrieves values) and auth_search (which searches), and it explains the unique AI-driven selection mechanism.

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 context for use ('when you need to pick the best credential for a task') and explicitly tells the caller to retrieve the value afterward with auth_get. It also describes graceful degradation when sampling is unsupported, but does not explicitly exclude alternatives or provide when-not-to-use guidance.

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