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scitex-ai

crossref-local

by scitex-ai
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Python
Hybrid

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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

    A3.6/5.0

    Scored across 17 tools

    Disambiguation4/5

    Most tools have distinct purposes (search, DOI lookup, cache management, visualization, status). However, cache_stats and cache_citation_summary overlap in providing citation statistics, and search_by_doi and enrich_dois both fetch metadata by DOI, which could confuse an agent.

    Naming Consistency4/5

    The naming generally follows a verb_noun pattern with clear prefixes like cache_, search_, check_, and enrich_. Minor inconsistencies exist, such as get_status vs. cache_* verbs and the crossref_local_skills_* prefix, but overall the pattern is predictable.

    Tool Count4/5

    17 tools is on the heavier side but appropriate for the scope: searching, enriching, caching, citation checking, visualization, and skill navigation. Each tool has a specific role, and the count is not excessive given the domain.

    Completeness4/5

    The tool surface covers core workflows: search, metadata enrichment, DOI validation, BibTeX checking, cache creation/querying/export/visualization, and status. Minor gaps exist, such as no cache deletion or cache update tool, but these are not critical for the primary use case.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

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