athena_mpc_plan

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

With no annotations provided, the description bears the full burden of behavioral disclosure. It discloses that the tool is PLAN_ONLY and never self-training, indicating no model updates or learning side effects. However, it does not mention what the tool returns, whether it modifies state, or any error-prone conditions, leaving significant behavioral aspects undisclosed.

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 two sentences with no filler. The main purpose is front-loaded, and the behavioral constraint is concise. Every word earns its place, making it appropriately sized and well-structured.

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 complex planning tool with no output schema, no annotations, and a large sibling set, this description is incomplete. It fails to explain what the output represents (e.g., a plan sequence or a value), the practical meaning of receding-horizon, or the roles of the unexplained parameters, leaving the agent without crucial information for correct invocation.

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 description coverage is 0%, so the description must compensate. It partially maps 'caller-supplied actions' to the actions parameter and 'receding-horizon' to the horizon parameter, and 'risk-adjusted' hints at risk_aversion. However, discount, beam_width, prior_strength, and initial_context remain completely unexplained, and with 7 parameters, this is insufficient for agent understanding.

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 a specific verb ('planning') and resource ('caller-supplied actions using the learned multivariate transition surface'), with qualifiers ('receding-horizon', 'risk-adjusted') that distinguish it from other planning tools like collective_plan or dual_control_plan. The 'PLAN_ONLY' constraint adds further specificity.

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 for risk-adjusted sequential planning over caller-supplied actions, but it does not explicitly compare with alternative planning tools or state when not to use it. The 'never self-training' phrase provides a hint that this tool is not for learning tasks, but no exclusions or alternative recommendations are given.

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