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fanar-mcp

by Blade-Labs
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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

    A3.7/5.0

    Scored across 12 tools

    Disambiguation4/5

    Each tool targets a distinct modality or task (chat, QA, translation, TTS, STT, image, moderation, poetry), and the deep_research/deep_research_result pair is clearly start-vs-poll. The only mild overlap is fanar_chat vs fanar_islamic_qa, since both answer questions with a language model, though the descriptions do distinguish grounded-citation QA from general chat.

    Naming Consistency4/5

    All tools use a consistent fanar_ snake_case prefix with a predictable verb/noun structure (list_models, validate_quotations, generate_image, deep_research_result). A few names are bare verbs or nouns (chat, translate, transcribe, moderate, poem) rather than verb_noun, but the convention is still readable and uniform.

    Tool Count5/5

    Twelve tools is well-scoped for a multimodal AI API, with one tool per capability and a clean async pair for deep research. No redundant or filler tools appear to pad the count.

    Completeness4/5

    The surface covers the major modalities (chat, QA, quotation validation, research, image, translation, TTS, STT, moderation, poetry) plus model listing and a full start/poll lifecycle for deep research. Minor gaps exist, such as no job cancellation/listing or embedding endpoint, but core workflows are covered.

    Maintenance

    ActivityNo data
    ResponsivenessNo issues

    Resources