chatgpt_network_diagnostics

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

With no annotations provided, the description carries the full burden. It discloses that the tool is read-only ('读取'), that data is local and desensitized, and explicitly lists what is excluded (query parameters, cookies, request/response bodies). This is meaningful behavioral context beyond what the schema reveals.

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 well-structured sentence, front-loaded with the core action and resource, followed by precise inclusion/exclusion details. Every phrase earns its place; there is no redundant or filler content.

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 simple read-only diagnostics tool, the description is largely complete: it states the data source, the exact fields available, and the sensitive data that is intentionally absent. It does not explicitly describe the return envelope or ordering, but with one optional limit parameter and a clear field list, the agent can invoke it correctly.

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?

The description does not mention the only parameter, 'limit', or explain how it controls the returned records. Schema description coverage is 0%, so the description should compensate but does not. The schema does provide constraints (default 100, min 1, max 500), yet the agent gets no contextual help from the tool description itself.

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 ('读取' / read) and resource ('本地脱敏网络异常记录'), then enumerates exactly which fields are included. This clearly distinguishes it from sibling status/lifecycle tools and leaves no ambiguity about the tool's purpose.

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 provides clear context: it is for reading local desensitized network anomaly records. However, it does not explicitly say when to use this tool versus alternatives like chatgpt_status or chatgpt_circuit_breaker_status, nor does it state any exclusions or routing conditions.

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