neuron_search_traffic

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states the data source and output, but omits details such as read-only guarantees, retention limits of ring buffers, ordering of results, or behavior when no matches are found.

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 tight sentences, front-loaded with the verb 'Search', and every clause carries information about scope, matching, and output. No filler or repetition.

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?

Despite no output schema, the description explains the return shape (matching requests with context snippets) and the schema documents all parameters. The main missing pieces are explicit usage guidance and operational limitations, so it is nearly complete but not fully.

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 coverage is 100% and all parameters already have meaningful descriptions. The description only restates that query can be a string or regex and mentions context snippets, adding no material semantic detail 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 states a specific verb ('Search'), a specific resource ('captured response bodies in ring buffers'), and the return value ('matching requests with context snippets'). This clearly distinguishes it from siblings like neuron_get_requests or neuron_get_logs, which focus on different data types.

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 the tool is for finding a string or regex in captured traffic, but it gives no explicit when-to-use guidance or alternatives. An agent must infer when this tool is preferable to neuron_get_requests or neuron_get_logs, and no exclusion criteria are stated.

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