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

Annotations already cover the safety profile (readOnly=true, destructive=false, idempotent=true), so the description earns credit for going further: it discloses the result cap of 8 matches, the shape of each result (ticker, name, market), and the ambiguity risk of shared names. The NZC-are-charities caveat is genuinely non-obvious context an agent could not otherwise infer. It does not note behavior for zero matches or how ties are ordered.

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?

Front-loaded with the verb and examples, then results, then the market filter, then the NZC warning — a sensible priority order with no filler. It loses a point only because the enumerated market list restates the schema's description verbatim and makes the last third read as a run-on.

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?

With no output schema, the description carries the return-value burden and does so: cap of 8, per-result fields, and the disambiguation role of the market field. Combined with the annotations covering safety, nothing an agent needs in order to call this correctly is missing.

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 already 100%, so this is value added on top: the examples show that 'query' accepts both names and numeric tickers, and the description explains the purpose of 'market' as constraining the search to a single market rather than merely listing the codes. That causal framing is more than the schema's terse 'Only this market' note.

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 and resource ('look up the ticker for a company name or partial symbol') and immediately differentiates its role from siblings, which all take a known ticker and return data. Concrete input examples ('Apple', '2330', 'samsung') make the intended resource unmistakable.

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 clearly implies when to reach for this tool — before any fundamentals lookup, when you only have a name or fragment — and gives sample inputs that set expectations. It stops short of naming the sibling tools (get_latest_fundamentals, etc.) as the next step or stating exclusions, so the routing is inferred rather than explicit.

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