plan_to_goal

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

The description discloses the algorithmic approach (latent MPC, cross-entropy method) and that it returns the first action of the best plan. No annotations are provided, but the description adequately covers the tool's behavior and expected output.

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 concise: three sentences covering purpose, method, and usage. Every sentence adds value with no redundancy or filler.

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 the absence of annotations and output schema, the description sufficiently explains what the tool does and how to use it. It could be slightly improved by mentioning the return format (e.g., action shape), but it is largely 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?

The input schema has 0% description coverage, but the description clarifies that current_obs is the current observation and goal_obs is the goal observation. This adds necessary context beyond the raw array type.

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 the tool's purpose: planning the next action toward a goal via latent MPC. It explains the method (encoding observations, searching action sequences) and distinguishes it from sibling tools like encode_observation and predict_rollout.

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 explicitly advises calling the tool repeatedly for receding-horizon control. While it doesn't list when not to use it or compare to alternatives, the replanning guidance is clear and practical.

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