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by exa-labs

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    TDQS

    A3.5/5.0

    Scored across 8 tools

    Disambiguation3/5

    The tools have overlapping purposes that could cause confusion. For example, web_search_exa and deep_search_exa both perform web searches, with the latter adding natural language formatting, while crawling_exa and web_search_exa both handle URL content extraction. However, descriptions help differentiate some specialized tools like get_code_context_exa and linkedin_search_exa.

    Naming Consistency4/5

    Most tools follow a consistent snake_case pattern with a '_exa' suffix, such as company_research_exa and web_search_exa. The main deviation is deep_researcher_start and deep_researcher_check, which use a different naming style without the suffix, slightly breaking the pattern.

    Tool Count5/5

    With 8 tools, the count is well-scoped for a server focused on web research and data extraction. Each tool serves a distinct function within this domain, such as company research, LinkedIn searches, and code context retrieval, making the set appropriately sized.

    Completeness4/5

    The toolset covers core research workflows, including starting and checking deep research tasks, general and specialized searches, and content extraction. A minor gap exists in lacking explicit update or delete operations for research tasks, but agents can work around this by managing task IDs and polling.

    Maintenance

    ActivityActive
    ResponsivenessSlow