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ParseRail MCP

by kyisaiah47
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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

    B3.3/5.0

    Scored across 41 tools

    Disambiguation3/5

    Most tools have distinct target use cases, but there are several overlapping pairs: summarize/minutes, classify/categorize, parse/invoice/receipt, and reply/outreach/review_reply. Reading the descriptions disambiguates them, but an agent is likely to need to compare closely before selecting.

    Naming Consistency3/5

    The parserail_ prefix and snake_case style are consistent, which helps. However, the suffix convention is mixed: bare verbs (parse, split, rewrite), bare nouns (invoice, receipt, memory), and noun-first compounds (po_match, review_reply, product_copy), so there is no predictable verb_noun pattern.

    Tool Count2/5

    At 41 tools, this is far beyond the typical well-scoped 3-15 tool range. Each endpoint may earn its place in the underlying API, but the MCP surface is too heavy for an agent to navigate easily. It feels like a full API dump rather than a curated set.

    Completeness3/5

    For a broad all-purpose AI-processing server, coverage is extensive: documents, audio, images, memory, outreach, finance, and account status are all present. However, because the domain is sprawling and many tools are one-shot generation or dead-end workflows, there is no clear lifecycle for created artifacts. Minor gaps like translation or custom voice selection are noticeable but not severe.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources