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Splunk Intelligence MCP Server

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      Enables AI agents to interact seamlessly with Splunk environments through 20+ tools for search, analytics, data discovery, administration, and health monitoring. Features AI-powered troubleshooting workflows and supports multiple Splunk instances with production-ready security.
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      Enables AI assistants to search, manage, and analyze data in Splunk instances through the Model Context Protocol. Supports SPL queries, index management, alerts, dashboards, and more.
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    • F
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    TDQS

    A3.5/5.0

    Scored across 7 tools

    Disambiguation4/5

    Tools are mostly distinct: investigate_start initiates, get_findings retrieves current findings, hint injects hints, lsp_call_chain traces code, pause halts, query_examples provides example queries, submit_report submits a report. There is slight overlap between get_findings and the findings returned by investigate_start, but descriptions clarify the different usage points.

    Naming Consistency3/5

    All tools share the 'splunk__' prefix and use lowercase with underscores, but the verb-noun pattern is inconsistent: 'get_findings' (verb_noun), 'hint' (just verb), 'investigate_start' (verb_noun), 'lsp_call_chain' (noun_verb), 'pause' (verb), 'query_examples' (noun_verb), 'submit_report' (verb_noun). Some tools are single words, others have multiple parts, but overall readable.

    Tool Count4/5

    Seven tools is a reasonable count for a focused investigation server. It covers the core workflow without being excessive. A few more could be added (e.g., listing past investigations), but the current count is appropriate.

    Completeness4/5

    The tool set covers the main investigation lifecycle: start, get findings, inject hints, trace code, pause, get examples, and submit report. Missing operations like direct arbitrary SPL querying are partially handled through investigate_start and submit_report queries. Overall, it's well-scoped with minor gaps.

    Maintenance

    ActivityActive
    ResponsivenessNo issues