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

With no annotations, the description carries the burden and does add real value: it discloses the no-buy/sell-orders constraint and a daily per-account limit. However, it omits what happens when the limit is hit, latency, or response format, so coverage is partial.

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?

Three short, front-loaded clauses with no filler; the core purpose leads and the constraint follows. Efficient, though the parenthetical could be tightened.

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 single-param tool with no annotations and no output schema, the description covers purpose and two behavioral constraints but leaves the return shape and alternative-tool relationship unaddressed, so an agent must infer how the response looks.

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 coverage is 100% and the single 'question' parameter is fully documented in the schema, so baseline is 3. The description's mention of 'natural language' mildly reinforces the expected input but adds no syntax or format detail beyond the 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?

States a specific verb (ask) and resource (AI assistant, natural language, about your portfolio) with a scoped topic list (analysis, context, risks). This clearly distinguishes it from the get_* siblings that return raw data, though it doesn't name any sibling explicitly.

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?

Implies usage through 'in natural language, about your real portfolio (analysis, context, risks),' but never states when to prefer this over direct data tools like get_portfolio or get_pnl. No explicit when-not or alternative routing.

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