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flexorch-mcp

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by flexorch
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Python
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    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

    A4.8/5.0

    Scored across 8 tools

    Disambiguation5/5

    Each tool has a clearly distinct role in the pipeline: document.process starts a job, job.status polls it, job.result retrieves extracted fields, and dataset.* tools handle building, exporting, searching, indexing, and chunk retrieval. Even job.status and job.result are clearly separated by their polling vs. result-reading purposes.

    Naming Consistency5/5

    All tools follow a consistent <domain>.<operation> pattern (document., job., dataset.) with lowercase snake_case. The operations mix verbs (process, build, export, index) and nouns (status, result, search, chunks), but the pattern is uniform and predictable, making it easy to infer the tool's function from its name.

    Tool Count5/5

    Eight tools is well-scoped for the server's purpose: a document processing and RAG preparation pipeline. Each tool covers a necessary step in the workflow, with no redundancy and no unnecessary additions.

    Completeness5/5

    The toolset fully covers the document processing lifecycle: submit document, monitor job, retrieve results, build dataset, export dataset, plus additional search/index/chunks capabilities for RAG. The workflow is clearly described with numbered steps, and there are no dead ends—every tool's output feeds into the next appropriate tool.

    Maintenance

    ActivityActive
    ResponsivenessNo issues

    Resources