check_permission

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It fully discloses the return structure (allowed bool, reason code, explanation, domain stance, signals), enumerates all reason codes and their meanings, explains the bot_blocked condition, and clarifies that no_policy/unspecified result in block behavior. This is exemplary transparency.

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 longer than average but well-structured with a lead sentence, a return summary, a reason-code list, and an Args section. Each section earns its place; the reason-code enumeration is somewhat lengthy but valuable for interpreting results. It is appropriately sized for the tool's complexity.

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 has three parameters, an output schema, and requires nuanced policy handling. The description covers the input format, allowed actions, return values, and all possible decision outcomes, including error cases. There is no significant missing context that an agent would need to invoke this correctly.

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?

Although the schema has no descriptions (0% schema coverage), the description thoroughly explains all three parameters: domain with an example, action with an explicit enum-like list, and agent_id with its optionality and behavioral effect (bot_blocked). It adds far more meaning than the bare schema, compensating completely.

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 uses a specific verb ('Check whether') and names the exact resource ('a specific action on a given domain'), and it clearly differentiates itself from sibling lookup tools by focusing on AI-agent permission decisions. It even enumerates the exact actions covered (scrape, summarize, train, search), making the purpose unmistakable.

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 explicit guidance: 'Use this before your agent scrapes, summarizes, trains, or searches content from a site.' It clearly states when to invoke the tool, though it doesn't explicitly name alternatives for cases where a plain domain lookup would suffice. Still, the intended usage context is well defined.

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