get_config_info

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

With no annotations provided, the description fully discloses behavior: it automatically tests API connectivity to /models endpoint, masks the API key, includes response time, and only includes available_models on success. This covers side effects, security, and output nuances.

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 well-structured with a clear opening sentence, bulleted use cases, a 'Returns' section detailing fields, and 'Notes'. It is detailed but every part earns its place, and the front-loaded purpose makes it scannable.

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 there is no output schema, the description thoroughly explains the return value structure and behavior. It also covers configuration details, connection testing, and security measures, making it 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?

With 0 parameters, the input schema is empty and the baseline is 4. The description additionally notes that no parameters are required, which adds clarity. No further parameter explanations are needed.

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 function with a specific verb and resource: 'Returns the current OpenAI Search MCP server configuration information and tests the connection.' This distinguishes it from sibling tools like web_search or switch_model.

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 explicit use cases ('Verifying that environment variables are correctly configured', 'Testing API connectivity', 'Debugging configuration issues'), which gives clear context. However, it does not explicitly mention alternatives or when not to use the tool, though sibling tools are clearly different in purpose.

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