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

No annotations are provided, so the description carries the full transparency burden. It states the tool 'Computes' and 'distils' data, implying a read-only lookup, but it never explicitly confirms that no mutations occur, nor does it mention authentication, rate limits, or potential side effects. The absence of any warning about destructive behavior is a minor gap, but given the zero annotations, more explicit safety disclosure would be ideal.

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 concise and well-structured. It opens with the core purpose, lists the specific outputs, and then contrasts with a sibling tool. Each sentence adds value without redundancy, making it easy to parse quickly while retaining all key information.

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 lacks an output schema, but the description enumerates the derived metrics and the final verdict, giving the agent a clear expectation of the result content. It also covers the input context and relationship to the raw-data tool. Minor omissions include error handling and output format details, but for a lookup tool of this complexity, the description is largely 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 schema coverage is 0%, but the description compensates by naming the parameter 'cui' and defining it as 'Romanian tax identification number' with an example ('2816464'). This provides sufficient semantic meaning for an agent to supply the correct input, though it could elaborate on format or required length.

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 explicitly states the tool 'Return[s] derived financial intelligence' and enumerates the computed metrics (revenue trend, growth, headcount, net margin, liquidity/distress signals, verdict). It also distinguishes itself from the sibling 'lookup_financials' by contrasting 'derived' vs 'raw' data, making its purpose unambiguous.

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 provides clear usage guidance by stating 'use lookup_financials for the underlying annual figures,' explicitly directing the agent to the alternative tool for raw data. This implies this tool should be used when derived metrics and a human-readable verdict are needed, effectively scoping the appropriate context.

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