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xncbf

ChatGPT MCP Server

by xncbf
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Python
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.2.1

  • Disambiguation2/5

    The 'ask_chatgpt_tool' returns the response immediately, making 'get_chatgpt_response_tool' redundant and causing confusion about when to use each. 'new_chatgpt_chat_tool' is distinct but the overlap between the first two tools leads to ambiguity.

    Naming Consistency4/5

    All tools follow a snake_case pattern with a '_tool' suffix, maintaining consistency. However, the noun component varies (no noun in 'ask_chatgpt_tool', 'response' in 'get_chatgpt_response_tool', 'chat' in 'new_chatgpt_chat_tool'), which is a minor deviation.

    Tool Count4/5

    Three tools is a reasonable number for a basic ChatGPT interaction server. It covers sending a prompt, retrieving the latest response, and starting a new conversation without being overly minimal or excessive.

    Completeness3/5

    The set covers sending prompts and managing conversations at a basic level, but lacks tools for listing past conversations, adjusting model parameters, or deleting chats, which are notable gaps for a chatbot interface.

  • Average 3.6/5 across 3 of 3 tools scored. Lowest: 2.8/5.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

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

    Without annotations, the description carries the full burden of behavioral disclosure, but it only states the basic action. No mention of side effects, statelessness, authentication, rate limits, or output format, leaving significant gaps for an agent.

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

    Conciseness4/5

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

    The description is a single, direct sentence with no waste. However, more informative content could be included without sacrificing conciseness.

    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?

    Given the simplicity of the tool (one parameter, no output schema), the description provides the core action. However, it lacks details about response format, error conditions, or conversation state, which are relevant even for a simple tool.

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

    Parameters2/5

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

    The description implies that the 'prompt' parameter is the text sent to ChatGPT, but it adds no extra meaning beyond the schema's type and title. With 0% schema coverage, the description should compensate but does not provide additional semantic or syntactic details.

    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 the action (send a prompt) and the resource (ChatGPT), explicitly mentioning the response. However, it does not differentiate from siblings like get_chatgpt_response_tool or new_chatgpt_chat_tool, missing explicit distinction.

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

    Usage Guidelines2/5

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

    No guidance is provided on when to use this tool versus siblings. An agent cannot determine whether to use ask_chatgpt_tool, get_chatgpt_response_tool, or new_chatgpt_chat_tool based on the description alone.

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

  • Behavior2/5

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

    No annotations are provided, so the description must carry the full burden. It only states the action without disclosing side effects like whether it creates a persistent conversation, destroys prior context, or requires authentication.

    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?

    Single sentence, front-loaded, no extra words. Perfectly concise for a simple tool.

    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?

    The description is adequate for a tool with no inputs and no output schema, but it fails to mention important details like whether the tool returns a conversation identifier or automatically activates the new chat, which would be helpful for an agent.

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

    Parameters4/5

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

    Input schema has zero parameters with 100% coverage. Baseline for 0 parameters is 4, and the description adds no parameter info, which is acceptable as none exist.

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

    Purpose5/5

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

    The description 'Start a new chat conversation in ChatGPT' clearly states the verb 'start' and resource 'chat conversation', and it distinguishes from siblings like 'ask_chatgpt_tool' which likely sends messages within an existing chat.

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

    Usage Guidelines4/5

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

    The description implies usage for initiating a conversation, and the context of siblings suggests it is the first step before using ask_chatgpt_tool, but no explicit when-not-to-use or alternatives are given.

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

  • Behavior4/5

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

    Despite no annotations, the description clearly indicates this is a read/retrieval operation. However, it does not disclose behavior when no prior message has been sent (e.g., returns null vs error), which is a minor gap.

    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, concise sentence that conveys the purpose without extraneous words.

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

    Completeness4/5

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

    For a tool with no parameters and no output schema, the description adequately explains the tool's action. It could be more complete by mentioning the format or behavior when no response is available, but it is sufficient for basic usage.

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

    Parameters4/5

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

    The input schema has zero parameters, and the description adds no parameter-level detail, which is acceptable per the baseline guideline (0 params = baseline 4).

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

    Purpose5/5

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

    The description uses a specific verb ('get') and resource ('latest response from ChatGPT'), and the context of siblings (ask_chatgpt_tool, new_chatgpt_chat_tool) clearly distinguishes this as a retrieval action after messaging.

    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 usage after sending a message ('after sending a message') but does not explicitly state when to use it vs alternatives, nor does it discuss prerequisites or error conditions if no prior message exists.

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

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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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