ChatGPT WebSearch MCP

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

No annotations are provided, so the description carries the full burden. It mentions model types and parameter support but fails to disclose critical behavioral traits such as rate limits, authentication needs, error handling, or what the response format looks like (since no output schema exists). The description is too vague for a tool with 11 parameters and complex functionality.

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 concise and front-loaded, stating the core purpose in the first sentence. The second sentence adds useful context about model types without redundancy. However, it could be slightly more structured by explicitly separating regular and reasoning model guidelines.

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?

Given the tool's complexity (11 parameters, no annotations, no output schema), the description is inadequate. It lacks details on response format, error conditions, rate limits, and practical usage scenarios. The high parameter count and absence of output schema require more comprehensive guidance than provided.

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 fully documents all 11 parameters. The description adds minimal value by hinting at model categories (regular vs. reasoning) but does not provide additional syntax, format details, or usage examples beyond what the schema already specifies. This meets the baseline for high schema coverage.

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 clearly states the tool's purpose: 'Ask ChatGPT a question and get a response.' It specifies the verb ('ask') and resource ('ChatGPT'), and distinguishes between model types (regular vs. reasoning). However, with no sibling tools, differentiation is not applicable, preventing a perfect score.

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 usage by mentioning support for both regular and reasoning models, suggesting when to use different parameter sets. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., for simple queries vs. complex reasoning), and there are no siblings to compare against, so the guidance is limited to internal model selection.

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