quantum_step

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

No annotations are provided, so the description carries the full burden. It does disclose meaningful behavior beyond the signature: amplitude amplification vs. destructive pruning, convergence testing, and returning the next priority branches. Still missing are the consequences of 'destructive pruning' (what state is discarded), whether the call mutates or terminates the session, and any error or rate-limit behavior. 'HOST AI CONTROL' signals a control-flow role but is unexplained jargon.

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 sentences, so it is not bloated, but the leading 'HOST AI CONTROL' token is unexplained jargon occupying the most prominent position, and the second sentence is a run-on chain of internal operations. Front-loading the caller's action before the engine's behavior would serve better.

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?

With no annotations, no output schema, and a seven-parameter iterative tool, the description should explain what comes back and how the loop ends. It gestures at the return ('the next priority branches') but does not describe termination, what happens on is_exact_solution=true, or what other fields the response carries. Adequate as a hint, insufficient as a specification.

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 100%, so the schema already documents all seven parameters, including evaluation_score's 0.0–1.0 range and the meaning of is_exact_solution. The description adds no parameter-level meaning beyond that — for example, how evaluation_score interacts with Grover amplification or when child_branches should be supplied. Baseline 3 applies.

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 core action is stated: the connected AI 'submits its evaluation for a quantum branch', which is a specific verb+resource. However, the description also bundles in the server-side processing (Grover amplification, pruning, convergence testing) without separating the caller's action from the tool's internals, and it never distinguishes this from siblings such as quantum_explore_branches or quantum_solve. An agent can guess the purpose but cannot cleanly place it in the workflow.

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?

There is no explicit when-to-use guidance and no mention of alternatives. The phrase 'returns the next priority branches' weakly implies a loop, but nothing says when to call this versus quantum_explore_branches, quantum_solve, or quantum_session_status, nor what preconditions (an active session from quantum_init_task) are required — that requirement is only inferable from the session_id schema description.

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