llmtrim_compress

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

No annotations provided, but description discloses input/output shape, token count reporting, and per-stage breakdown. It does not mention auth or rate limits, but for a compression tool, the behavior is sufficiently transparent.

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?

Two sentences, front-loaded with purpose, no wasted words. Every sentence provides critical information.

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 tool with 2 parameters and no output schema, the description covers input expectations, output shape (compressed request, token counts, breakdown), and usage context, making it complete.

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 coverage is 100%, but description adds value by clarifying that 'request' accepts both JSON object and string, and that 'provider' parameter is an optional hint. It also compensates for missing output schema by describing return value structure.

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 specific verb 'compress' and resource 'LLM request body', and explicitly states input and output format, distinguishing it from siblings like llmtrim_compress_text and llmtrim_stats.

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?

It clearly states when to use: pass raw request JSON to get compressed output and token savings. Sibling names imply alternatives, but no explicit exclusions or when-not-to-use guidance.

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