fill_placeholders

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

No annotations provided, so description carries full burden. It discloses the two modes and that expand uses LLM API, but lacks detail on side effects, error handling, rate limits, or state modification. Adequate but not comprehensive.

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 efficient sentences covering purpose, modes, and context. No redundancy or irrelevant details; each sentence earns its place.

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?

Given nested objects, two modes, no output schema, and no annotations, the description explains modes and that expand requires LLM config but omits output format, error conditions, and prerequisites. Adequate but incomplete for a tool with this complexity.

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 has 100% coverage with parameter descriptions. Description adds a concise summary of modes but does not significantly extend meaning beyond the schema. Baseline 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?

Description clearly states the tool fills XML placeholders in templates, explicitly names two modes (direct and expand), and positions itself as the core orchestration tool for LLM text generation, differentiating it from siblings like generate_image.

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?

Mentions two modes and that expand calls LLM API, providing some context for use. However, it does not explicitly state when to choose direct vs expand, nor when to avoid this tool (e.g., for image generation). Guidance is implied but not fully explicit.

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