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openai-decisions-mcp

openai-decisions-mcp

by semanticist21
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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.9/5.0

    Scored across 3 tools

    Disambiguation3/5

    find_code is clearly distinct, but decide and evaluate_batch both perform batched paid OpenAI evaluations over text/files/images/PDFs. The distinction between multiple questions on shared evidence vs. common questions across many named items is useful but subtle enough that an agent could misselect.

    Naming Consistency4/5

    All names use lowercase snake_case and start with an imperative verb, giving a mostly predictable pattern. decide is a bare verb while evaluate_batch and find_code include an object noun, which is a minor inconsistency.

    Tool Count4/5

    Three tools is compact and each covers a meaningful operation, fitting the server's focused evaluation purpose. The slight overlap between decide and evaluate_batch keeps it from being a perfectly scoped set.

    Completeness4/5

    The surface covers single/batched evaluation across text, files, images, PDFs, and code relevance search, which is solid for an OpenAI decision/evaluation server. Minor gaps include reliance on external file listing for code discovery and no built-in project/file inventory.

    Maintenance

    ActivityNo data
    ResponsivenessOutdated data

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