find_undervalued_companies

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

With no annotations, the description carries the full burden and does so thoroughly. It discloses ranking normalization (min-max across candidate list only, not absolute), the inability to discover a universe, handling of unresolved candidates (appended, not dropped), and the semantics of max_results (failed candidates don't count). This goes well beyond the schema and gives the agent accurate expectations.

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 efficiently structured: the core purpose is front-loaded, followed by a critical limitation, then key behavioral details and parameter documentation. Every sentence adds essential information without redundancy or filler, making it easy for an agent to parse and internalize.

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?

Given the tool's moderate complexity, two undocumented parameters, and no annotations, the description is remarkably complete. It covers input constraints, ranking methodology, score interpretation, and edge-case handling. Since an output schema exists, the description does not need to explain return fields, and the provided behavioral context is sufficient for correct invocation.

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 description coverage is 0%, so the description must fully document parameters. It does: symbols are defined as candidate ticker symbols with a max-15 constraint and 'extras are dropped', and max_results is explained with the crucial detail that unresolved candidates don't count against the limit. This adds significant meaning beyond the bare type and default in the schema.

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 states a specific verb ('Screen'), a specific resource ('explicit list of NSE/BSE-listed Indian equities'), and the exact purpose ('undervaluation signals'). It further distinguishes itself by noting it cannot discover a universe and requires supplied candidates, which separates it from potential sibling screeners like find_high_quality_companies.

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 specifies the precondition: the user must supply candidate symbols because the tool cannot discover a universe on its own. It also notes the max 15 candidates and the fallback behavior for unresolved items, providing solid context for when to use this tool, though it does not explicitly name or exclude alternative sibling tools.

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