neuron_snapshot_state

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

With no annotations, the description carries the burden of behavioral disclosure. It transparently states that the snapshot is stored in memory with a label, which implies a side effect. However, it does not clarify behavior such as whether labels overwrite, how long snapshots persist, or what happens on failure.

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. It front-loads the core action, then adds the comparison use case and examples in the second sentence. Every 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?

For a tool with a simple schema and clear purpose, the description is largely complete: it covers what is captured, where it is stored, and how to use it. Since there is no output schema, it could additionally state what the tool returns (e.g., the label), but the description still gives enough for an agent to proceed.

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 both parameters adequately. The description adds context that the label is used for later comparison, but doesn't add substantial meaning beyond the schema.

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 ('Capture a snapshot') and specifies the resource ('current page state') with a concrete list of captured components. It also names its companion tool (neuron_diff_states), helping an agent distinguish it from visual or performance snapshots like neuron_screenshot and neuron_perf_snapshot.

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 gives clear context: it is for capturing state and later comparing with neuron_diff_states, with explicit use cases like SPA testing and dynamic content tracking. It doesn't explicitly state when not to use alternatives, but the pairing with diff_states provides strong guidance.

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