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

The readOnlyHint annotation already covers safety, and the description adds meaningful behavioral context: the data is live, account-dependent, and sourced from the web UI rather than a hard-coded list. This helps an agent understand that results may change and should not be assumed static.

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?

Two sentences with no filler. The main action is front-loaded, and the second sentence adds an important caveat about the source of truth without unnecessary detail.

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 zero-parameter read-only introspection tool with an output schema and readOnly annotation, the description fully covers what the agent needs: purpose, source of truth, and dynamic nature. Nothing critical is missing.

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 tool has zero parametershare, so there is no parameter burden on the description. Schema coverage is complete, and the description appropriately focuses on what is being inspected rather than input details.

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 (inspect) and resource (live model and reasoning/effort choices for the signed-in ChatGPT account). This clearly differentiates it from sibling tools like chatgpt_status and chatgpt_chat, which are about status and chat operations.

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 when to use this tool: when an agent needs the live, account-visible model and reasoning/effort capabilities rather than chat or status. It does not explicitly name alternatives or exclusions, but the context is clear enough for an agent to route correctly.

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

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

Beyond the sparse annotations, the description adds useful behavioral caveats: ChatGPT cannot access repositories or execute code, and returned code must be validated. It does not cover authentication, rate limits, or conversation persistence, but it does reveal non-obvious constraints.

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 compact and front-loaded with the core action. Each sentence earns its place: the action, the capability boundary, and the caller responsibility.

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?

The output schema presumably covers return values, but the input side is incomplete for a 5-parameter tool: the meaning of effort, model, timeout_ms, and conversation_id is absent. The behavior caveats are helpful, but an agent still lacks enough detail to use optional parameters 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% schema description coverage, the description needed to compensate, but only 'prompt text' clarifies the prompt parameter. 'model', 'effort', 'timeout_ms', and 'conversation_id' are left to be inferred from their names alone.

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 specifies the action ('Send prompt text'), the resource ('ChatGPT Web'), and the result ('return the assistant response'), making the tool's function clear. It does not explicitly contrast with chatgpt_status or chatgpt_capabilities, but the distinct action leaves little room for confusion.

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 should be used whenever a ChatGPT response is needed and warns that repository/execution capabilities are absent, acting as a when-not-to-use boundary. It never names the sibling tools or states conditions for choosing status/capabilities instead.

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

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

With readOnlyHint=true already present, the description adds useful context by guaranteeing that sensitive data is not exposed. It also clarifies that this is a status check rather than a data-access operation, which helps set expectations.

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?

Two concise sentences with no filler. The primary purpose is front-loaded, and the second sentence adds a valuable privacy clarification.

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?

This is a simple zero-parameter tool with an output schema and a read-only annotation. The description covers what the tool checks and what it does not expose, which is sufficient for an agent to select and 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 tool has zero parameters, so the schema provides full coverage trivially. The description appropriately focuses on the tool's behavior rather than parameter details, which are unnecessary here.

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 gives a specific verb and resource: checking whether the persistent ChatGPT Web session is authenticated and usable. It also explicitly distinguishes itself from data-exposing tools by stating it does not expose cookies, tokens, profile files, or workspace data.

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 intended use is reasonably clear: verify session status before relying on the ChatGPT Web session. However, it does not explicitly name alternatives like chatgpt_capabilities or chatgpt_chat, nor does it state when not to use this tool.

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