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

With no annotations provided, the description carries the full burden and does so excellently. It discloses the retrieval + synthesis pipeline, the return structure ({answer, sources, model}), the nature of the citations, and an important edge case: an empty workspace yields {answer, empty_brain: true} with no sources, which is a statement about the workspace rather than a failed search. This anticipates misinterpretation and guides the agent's next action.

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 front-loaded with the primary purpose, then organized into usage, arguments, and return behavior. Every sentence adds value: no filler, no repetition of schema names, and the important empty-brain caveat is placed at the end where it serves as a critical clarification.

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?

The tool has only 2 parameters (1 required) and an output schema, and the description covers both parameter semantics and return values thoroughly. It also handles the non-obvious empty-workspace case, making it complete for an agent to select and invoke the tool correctly without further context.

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%, but the description compensates fully. It defines 'question' as a natural-language question and explains 'model' as an optional override with concrete examples (gemini-2.5-flash, gpt-4o, claude-sonnet-4-5) and the default-selection behavior for team vs. personal workspaces. This adds meaning far beyond the bare schema.

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 specific verb and resource: 'Ask Enhanciar a natural-language question grounded in the key's workspace.' It further clarifies scope with concrete examples ('what does X do', 'why did we choose Y') and frames itself as 'the high-level tool' running the full retrieval + synthesis pipeline, distinguishing it from the more specific sibling tools like get_page, get_skill, and search_wiki.

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?

It gives clear when-to-use guidance: 'use it for any what does X do / why did we choose Y / where is Z handled question.' It also explains that it is the high-level tool vs. lower-level siblings, but it does not explicitly name alternatives or state when not to use it, so it falls just short of a 5.

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