chatgpt-web-mcp

by Goudu666

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JavaScript

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chatgpt_select_answer_tier

Select the answer tier for ChatGPT responses, including the Pro tier, and check that the page displays it correctly.

Instructions

选择输入框右侧的能力档位。当前支持精确选择配置的最高档“Pro”，并校验页面显示结果。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "answerTier": "<answerTier>"
    },
    "name": "chatgpt_select_answer_tier"
  }
}

Input Schema

Name	Required	Description	Default
`answerTier`	Yes	能力档位名称；最高档默认为“Pro”。

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 14, 2026

TDQS

A3.5/5.0

Behavior3/5

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 discloses that the tool selects a tier and validates the page display result, which is useful. However, it does not mention side effects, failure behavior for unsupported tiers, or prerequisites such as an open browser or an active ChatGPT session.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single efficient sentence that front-loads the action and constraint, and it adds the verification behavior without any filler. Every part contributes meaningful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool, the core behavior and current constraint are covered, and the verification step is mentioned. However, with no output schema or annotations, return values, error behavior, and prerequisites are left unspecified, which an agent may need to know before invoking the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single parameter, and the tool description reinforces the meaning by stating that 'Pro' is the highest/default tier. This adds a small constraint beyond the schema, but it does not elaborate on accepted value formats or edge cases.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb ('选择' / select), a precise resource ('输入框右侧的能力档位' / capability tier beside the input box), and the currently supported value ('Pro'). It is easily distinguishable from sibling tools that list or manage modes, models, or thinking levels, though it does not explicitly name them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies that this tool should be used when selecting the answer capability tier and notes that only the highest tier 'Pro' is currently supported. However, it provides no explicit guidance about when to use this tool versus alternatives such as chatgpt_answer_tier_status or chatgpt_select_mode.

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