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

No annotations are provided, so the description carries the full burden. It discloses the data source (Financial Modeling Prep), the specific scoring thresholds (Altman-Z <1.8 distress, >3 safe; Piotroski 0-9), and risk flags (OTC/pink-sheet, penny-stock, micro-cap). It does not mention auth requirements or return structure, but for a read-only data lookup the key behavioral traits are well covered.

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 front-loaded with the purpose, then provides detailed metric explanations with thresholds, followed by explicit usage guidance and input type. Every sentence contributes meaningful information without filler, achieving a good balance between detail and brevity.

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 compensates by enumerating the metrics and their interpretations (e.g., Altman-Z ranges, Piotroski range). It doesn't describe the response structure (e.g., JSON keys), but for a simple one-parameter lookup tool, it provides enough context for an agent to understand what information will be returned and how to use it.

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 already describes the query parameter as 'a ticker symbol or company name' with 100% coverage. The description merely restates this as 'Takes a ticker or company name.' No additional semantic nuance (e.g., case sensitivity, format, examples) is added, so the baseline score of 3 applies.

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 provides a 'Stock RISK/safety profile' and enumerates specific metrics (actively trading vs delisted, exchange, penny-stock flags, Altman-Z, Piotroski). It explicitly positions itself as 'the stock analogue of token_security', which distinguishes it from sibling stock tools like stock_quote and stock_movers.

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 states 'Use for ... questions' with concrete examples like 'is this stock safe / legit / going to zero / a pump-and-dump', and names token_security as an analogue. However, it does not explicitly state when not to use it (e.g., for price quotes or market movers), so the guidance is clear but not exhaustive.

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