optimize_agent_prompt

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says the tool 'formulates' prompts, revealing nothing about side effects, persistence, auth requirements, read-only status, or return semantics. Since annotations are absent, the description leaves the safety and mutation profile entirely unspecified, which is a meaningful gap.

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?

A single sentence that front-loads the purpose and packs useful examples without excessive fluff. The parenthetical examples earn their place by signaling the enum domain. It's slightly long but well-organized for a tool with only two parameters.

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 simple two-parameter tool with full schema coverage, the description covers the core purpose adequately. However, with no output schema and no annotations, it omits what the returned prompt template looks like, whether it returns a single prompt or variations, and any behavioral caveats. Adequate but with clear gaps around output expectations.

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%, so the schema already documents both parameters. The description adds marginal value by giving example use cases that map to enum values (Code Refactoring → code_refactoring, Bug Fixes → bug_investigation), which helps an agent pick a task_type. Baseline 3 is appropriate since the schema handles the heavy lifting and the description's contribution is modest.

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 states a specific verb ('formulates'), a clear resource ('system prompts and prompt templates'), and gives concrete examples (Code Refactoring, RAG Search, Multi-Agent Orchestration, Bug Fixes) that map to the enum values. It distinguishes itself reasonably from sibling 'generate_*_blueprint' tools, which produce domain plans, though it doesn't explicitly call out the difference. A slight deduction because the examples slightly blur the line with siblings like generate_ai_workflow_orchestration.

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 implies usage context through examples of complex AI agent tasks, which gives an agent a rough sense of applicability. However, it offers no explicit when-to-use vs. when-not-to-use guidance and names no alternative tools, relying on the reader to infer scope. For a tool with 60+ sibling generation tools, explicit routing would materially help.

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