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gto1seoul

MCP OpenAI Server

by gto1seoul
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JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.

    Naming Consistency5/5

    A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'openai_chat' follows a clear verb_noun pattern.

    Tool Count2/5

    One tool is too few for a server named 'MCP OpenAI Server', which suggests broader OpenAI capabilities. A single chat tool feels thin and under-scoped for the implied domain, lacking operations like embeddings, image generation, or fine-tuning.

    Completeness2/5

    The tool surface is severely incomplete for an OpenAI server. It only covers chat completions, missing obvious gaps such as embeddings, vision, audio, file operations, and model management, which are core to OpenAI's API offerings.

  • Average 3.5/5 across 1 of 1 tools scored.

    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
    • No stable releases found
    • 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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. The description mentions the action ('sends messages') and model options, but it doesn't disclose key behavioral traits such as authentication requirements, rate limits, error handling, or what the response looks like (e.g., format, content). For a tool interacting with an external API, this is a significant gap in transparency.

    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 concise and well-structured with two sentences: the first sets usage context, and the second explains the core action. Every sentence adds value without redundancy, making it efficient and front-loaded for quick comprehension.

    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 tool's complexity (interfacing with an external API) and the absence of annotations and output schema, the description is moderately complete. It covers purpose and usage but lacks details on behavior, error handling, and response format. For a tool with no structured safety or output information, it should provide more context to be fully helpful.

    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?

    The input schema has 100% description coverage, with clear documentation for both parameters (messages and model). The description adds minimal semantic value beyond the schema—it reiterates the model options but doesn't explain parameter interactions or usage nuances. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.

    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 tool's purpose: 'sends messages to OpenAI's chat completion API using the specified model.' It specifies the verb ('sends messages') and resource ('OpenAI's chat completion API'), but since there are no sibling tools, it doesn't need to differentiate from alternatives. It's specific but lacks sibling context, which isn't required here.

    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 provides explicit guidance on when to use this tool: 'when a user specifically requests to use one of OpenAI's models (gpt-4o, gpt-4o-mini, o1-preview, o1-mini).' This gives clear context for usage. However, it doesn't mention when NOT to use it or discuss alternatives, which is less critical since no sibling tools exist.

    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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  • Evaluate tool definition quality.

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