lrem

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

With no annotations, the description explains removal behavior, count semantics (0=all, positive=from head, negative=from tail), and return value, but could mention that it modifies the list in place.

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?

Five lines with clean 'Args' and 'Returns' structure, no wasted words.

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?

Covers parameters and return value adequately; missing potential error cases (key not a list) but acceptable for a simple 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 coverage is 0%, but description explains all three parameters with meaningful context (count semantics, element removal), compensating well.

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 'Remove elements from a Redis list' with a specific verb and resource, distinguishing it from siblings like lpop or rpop.

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?

No guidance on when to use lrem over alternative list operations (lpop, rpop, etc.) or when not to use it.

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