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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds the specific behavioral detail of comparing to a pre-AI baseline. It does not contradict annotations, though it could disclose dependencies (e.g., requiring a baseline to be set).

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 three concise sentences, front-loaded with the primary purpose. Each sentence adds value: the first states the action, the second explains the output, and the third provides example questions. 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?

The tool has an output schema, so return values are covered. The description adequately explains the core functionality and comparison logic, but could be more complete by mentioning the dependency on a pre-AI baseline or the optional filters. Overall, sufficient for a read-only analytics 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?

The input schema provides 100% coverage of all four parameters with clear descriptions (period, workspace, agent_name, process_name). The description does not add additional meaning beyond the schema, so the baseline score of 3 is appropriate.

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 opens with a specific verb ('Analyze') and resource ('cost-per-decision for AI agents'), then clarifies it shows actual AI cost relative to a pre-AI baseline. This clearly differentiates it from other cost-related siblings like thinkneo_check_spend or thinkneo_agent_roi.

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 implicitly indicates use for cost-per-decision questions, but does not explicitly contrast with sibling tools (e.g., thinkneo_agent_roi, thinkneo_get_savings_report) or state when not to use it. There is no guidance on prerequisites like setting a baseline.

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