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

With no annotations, the description carries the full burden of behavioral disclosure. It names the loop phases but does not say whether the tool mutates state, whether it invokes other tools internally, what environment it operates on by default, or what termination/outcome behavior looks like.

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 a single front-loaded sentence with no filler, and the pipeline enumeration adds useful specificity. It is concise without being tautological, though some of its brevity comes at the cost of missing behavioral detail.

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?

This is a complex orchestration tool with four parameters and no annotations. The output schema reduces the need to explain return values, but the description still omits prerequisites, parameter roles, defaults, side effects, and when to invoke this tool versus siblings, leaving significant gaps.

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%, and the description adds no meaning for env_id, actions, env_type, or max_steps. The schema provides names and defaults, but an agent gets no help understanding how each parameter affects the closed-loop run.

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 uses a specific verb ('Run') and a clear resource ('end-to-end cognitive agent closed loop'), and it elaborates the pipeline as 'Perceive -> Model -> Decide -> Act -> Reflect -> Learn', which conveys the tool's scope well. However, it does not explicitly differentiate from the similarly named sibling cognitive.run_closed_loop_agent or from run_multi_agent_simulation, so it misses the top score.

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?

No guidance is given about when to use this tool versus alternatives such as cognitive.run_multi_agent_simulation or cognitive.execute_task. The word 'end-to-end' implies a full-loop use case, but the conditions and exclusions are left entirely to inference.

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