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

With no annotations, the description should disclose behavioral traits. It only lists the returned fields but does not mention that this is a read-only, safe operation, nor does it address authentication requirements or potential failure modes (e.g., if no policy exists). The description is minimal.

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 sentence that is front-loaded with the main action and lists key details. No extraneous words, making it highly efficient.

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?

For a zero-parameter retrieval tool with no output schema, the description adequately explains what the tool returns. However, it could mention that the policy is per-agent or that it requires an active session, given the sibling tools imply a complex system.

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 tool has zero parameters, placing the baseline at 4. The description adds value by enumerating the specific policy fields returned, which helps the agent understand the output format even though there is no output 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 specifies exactly what the tool retrieves ('current AI Agent Tokenized Stock OS agent policy') and lists the policy elements (max notional, slippage, simulate-first, kill switch). This clearly distinguishes it from sibling tools like stocktoken_policy_set and stocktoken_get.

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 states what the tool does but provides no explicit guidance on when to use it versus alternatives (e.g., stocktoken_policy_set for modification, other get tools for different data). Usage is implied as a read operation, but no exclusions or context are given.

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