get_issuer

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

With no annotations present, the description carries the full disclosure burden. 'Research-only' conveys a read-only, side-effect-free operation, and 'when present' acknowledges optionality in the data. It lacks details on unknown-ticker behavior or potential fetch failures, but for a simple getter this is solid.

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?

A single, front-loaded sentence with no filler: the main action is stated first, with the content list in parentheses. Every word earns its place.

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 one-parameter, read-only tool with an output schema present, the description is sufficient: it names the source, target scope, data fields, and the research-only restriction. It does not discuss unknown-ticker behavior or contrast with siblings, but these are minor given the schema and sibling names.

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 ticker parameter already includes examples (SNTS, ORAC, ECOC), so the description adds no extra parameter semantics. The baseline of 3 applies because the schema handles the heavy lifting.

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 names a specific verb ('Get') and resource ('brvm.org issuer profile') and enumerates the content: legal name, sector, listing date, and contacts when present. This clearly differentiates it from sibling tools like get_quote and get_ohlcv, which concern pricing and history.

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 phrase 'Research-only' signals the appropriate context, and the described output makes plain that this tool serves fundamental-data needs rather than market data. However, it does not explicitly name alternatives or state when not to use it, so it provides clear context without exclusions.

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