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pierrebrunelle

OpenAI MCP Server

by pierrebrunelle
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'ask-openai' has a clear and singular purpose, making it impossible for an agent to misselect between tools.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'ask-openai' follows a verb_noun pattern, and with no other tools to compare against, there are no inconsistencies.

    Tool Count2/5

    A single tool is generally too few for a server's purpose, as it limits functionality and scope. For an 'OpenAI MCP Server', one might expect more comprehensive coverage such as different model interactions, fine-tuning, or other API endpoints, making this feel thin and under-scoped.

    Completeness1/5

    The tool set is severely incomplete for an 'OpenAI MCP Server'. With only a direct question tool, it lacks essential operations like model listing, chat completions, embeddings, or file handling, which are core to OpenAI's API. This will likely cause agent failures due to missing functionality.

  • Average 2.4/5 across 1 of 1 tools scored.

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

    • No issues in the last 6 months
    • No commit activity data available
    • 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.

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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. It mentions 'ask' and 'direct question,' implying a read-only query, but fails to detail authentication needs, rate limits, response format, or potential costs. This is a significant gap for an AI interaction tool with no annotation coverage.

    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, efficient sentence with no wasted words, making it appropriately concise. However, it lacks front-loading of critical information, as it doesn't immediately clarify the tool's core function beyond a vague phrase.

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

    Completeness2/5

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

    Given the complexity of interacting with AI models, no annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't address behavioral traits, parameter meanings, or expected outputs, leaving the agent under-informed for effective tool use.

    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?

    Schema description coverage is low at 25%, with only the 'query' parameter having a minimal description ('Ask assistant'). The tool description adds no parameter semantics beyond what the schema provides, failing to compensate for the coverage gap. It doesn't explain the purpose of model selection, temperature, or max_tokens.

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

    Purpose3/5

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

    The description 'Ask my assistant models a direct question' states a purpose (asking questions to AI models) but is vague about what 'assistant models' refers to and lacks specificity about the resource or scope. It doesn't distinguish from siblings (none exist), but the phrasing is somewhat unclear rather than tautological.

    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?

    The description provides no guidance on when to use this tool versus alternatives, prerequisites, or exclusions. It merely states what the tool does without context for application, leaving the agent to infer usage scenarios.

    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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