intelligence_moderate_content

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

Annotations already declare readOnly, idempotent, and destructive=false. The description adds important behavioral context beyond annotations: it uses the client's LLM and makes no Discord API call, which implies token costs and internal processing rather than API side effects. This is meaningful but does not cover potential LLM non-determinism or error cases, so a 4 is appropriate.

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 extremely concise and well-structured with bolded 'Purpose', 'When to use', and 'Returns' sections. Every sentence earns its place; no fluff or redundancy.

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 a rich output schema (indicated), so the Returns section is redundant but harmless. The description covers purpose, usage, and return shape completely. It could mention prerequisites (e.g., client LLM availability) or limitations, but given the annotations and schema, it is sufficiently complete for an agent to select and invoke the tool.

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 100%: 'content' is described as 'Text to moderate' and 'policy' as 'Moderation policy in plain language' with a default. The description's mention of 'plain-language moderation policy' adds no new parameter meaning beyond the schema, so the baseline 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 verb ('Apply') and resource ('a plain-language moderation policy to a piece of text'), and explicitly distinguishes it from Discord API calls and other intelligence tools by calling it 'purely a moderation utility' and naming the return decision (allow/flag/block). This differentiates it from siblings like intelligence_classify_messages and intelligence_summarize_channel.

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 'When to use' section lists three explicit scenarios: pre-check user-submitted content, second-opinion on AutoMod decisions, and classifying ambiguous messages. This gives clear context but does not mention when NOT to use or alternative tools, so it falls short of a full 5.

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