moat_assessment

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

With no annotations, the description must disclose behavioral traits. It states the tool returns a heuristic score and signals, but does not explain side effects, data source, freshness, or whether it is read-only. This leaves significant behavioral 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?

The description is a single salient sentence, front-loaded with key information. It could be more structured but remains concise and to the point.

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 has no annotations, no output schema, and simple parameters, the description should cover usage context, score interpretation, and parameter constraints. It only offers a high-level summary, missing critical details 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?

The schema has 0% description coverage for parameters. The description adds no meaning to 'ticker' or 'market' (e.g., what 'market' values like 'IN' represent). The agent must guess from names, which is insufficient.

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's subject (economic-moat signals) and output (0-10 heuristic score), with examples of signal types (brand, cost, scale, IP). It effectively distinguishes from sibling tools like 'score_company' and 'risk_assessment' by specifying the moat focus.

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 no guidance on when to use this tool versus alternatives, such as 'analyze_company' or 'risk_assessment'. It lacks any 'use when' or 'prefer X for' statements, leaving the agent to infer context.

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