chatgpt_list_history

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

With no annotations provided, the description carries the full burden. It clearly indicates a non-mutating list operation and discloses the return format, but it does not describe any edge cases, pagination behavior, or the effect of the limit parameter. This is adequate for a simple read-only list operation but lacks depth.

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 sentence that front-loads the core action ('列出...历史对话'), states the filtering option, and lists the return fields. Every word earns its place, with no redundancy or fluff.

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 list operation with two optional parameters and no output schema, the description adequately covers the scope, filtering behavior, and return fields. It could explain how limit applies or what happens when no conversations are loaded, but those are minor gaps. The description is reasonably complete for an agent to invoke the tool 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?

Schema coverage is 0%, so the description must illuminate the parameters. The phrase '可按标题筛选' directly clarifies that the query parameter filters by title, which is useful. However, the limit parameter is not mentioned at all, and the query parameter is not explicitly named. The schema already provides limit's default/range, so this is partial compensation.

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 action: listing ChatGPT side-bar history conversations that are currently loaded, with optional filtering by title. It also names the return fields (title, conversationId, URL), making the tool's function completely unambiguous. While it doesn't explicitly mention sibling tools, the 'currently loaded in sidebar' scope differentiates it from search_history or select_history.

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 a use case (retrieve currently loaded conversations) but provides no explicit guidance on when to choose this tool over alternatives like chatgpt_search_history or chatgpt_select_history. There are no exclusion criteria or references to sibling tools, leaving the agent to infer the appropriate context.

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