neuron_set_mock

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

With no annotations provided, the description carries the burden of behavioral disclosure. It states the key side effect: intercepting requests and returning a custom response, and importantly notes that the real backend is not touched. It does not mention rule persistence, precedence, or whether existing mocks are affected, but the core behavior is transparent.

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, front-loaded with the action and effect, then provides concrete usage examples. Every sentence earns its place with no redundancy.

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?

The tool has 8 parameters, no annotations, and no output schema, so the description needs to do more. It covers the basic purpose and use cases but omits important operational context such as how long a mock rule persists, how it interacts with existing mocks, and what the tool returns on success. The schema compensates for parameter details, but lifecycle and return-value gaps remain.

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 baseline is 3. The description's references to 'slow APIs' and 'custom response' loosely map to delay and responseBody, but it does not add meaningful parameter detail beyond what the schema already provides.

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 and resource: 'Add a network mock rule' and clearly explains what the tool does ('intercept requests matching a URL pattern and return a custom response'). This distinguishes it from sibling tools like neuron_get_mocks and neuron_clear_mocks.

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 explicit use cases: 'test error states, slow APIs, or custom payloads without touching the real backend.' It does not name alternative tools or state when not to use it, but the intended context is clear.

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