get_company

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

No annotations are provided, and the description does not disclose behavioral traits like read-only nature, authentication requirements, or rate limits. The tool likely performs a safe read, but the description does not confirm this, leaving gaps.

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?

Two sentences with no unnecessary words: first describes output, second provides usage guidance. It is front-loaded and efficiently uses space.

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?

An output schema exists (not shown), so return value documentation is covered. The description lists key data categories (sector, fundamentals, etc.), giving good context. Could optionally mention that it returns aggregate data, but overall complete.

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 input schema has 100% coverage with a clear description for 'identifier' (symbol, name, alias). The tool description does not add extra parameter meaning beyond what the schema already provides, so baseline 3 is appropriate.

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 clearly states the tool retrieves a company profile for a Saudi stock, listing included data types (sector, industry, fundamentals, etc.). It distinguishes itself from sibling tools like get_historical (historical data) or get_dividends (dividends only) by being comprehensive.

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?

Explicitly directs when to use: 'when the user asks about a company's profile, key metrics, or detailed information.' While it doesn't list alternatives for specific queries (e.g., dividends), the sibling list helps agents infer.

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