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"Using ChromaDB and Neo4j for persistent memory storage" matching MCP servers:

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  • F
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    maintenance
    Read-only Azure storage intelligence MCP server enabling scoped investigations, risk analytics, cost savings simulation, data health checks, and admin tenant-wide discovery across a synthetic storage estate.
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  • F
    license
    A
    quality
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    Provides persistent memory for MCP-compatible agents (like Copilot CLI) to save and recall knowledge across sessions, plus long-running monitoring tools.
    9
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  • A
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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.
    57
    27
    Apache 2.0
  • A
    license
    C
    quality
    D
    maintenance
    Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.
    40
    1
    Apache 2.0
  • A
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    quality
    D
    maintenance
    Enables AI agents to integrate with IBM Storage Insights for seamless observability and diagnosis of registered storage assets. It provides tools to retrieve alerts, performance metrics, and system configurations via the IBM Storage Insights External APIs.
    9
    Apache 2.0
  • A
    license
    Not graded
    quality
    A
    maintenance
    Shows your current Claude Desktop activity as Discord Rich Presence, including model, status, usage, and subscription info.
    MIT
  • A
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    Not graded
    quality
    D
    maintenance
    Enables Git repository operations and real-time monitoring via MCP tools, with support for WebSocket events, authentication, and observability.
    14 npm
    4
    MIT
  • A
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    Not graded
    quality
    C
    maintenance
    Exposes a synthetic issue tracker and pipeline warehouse as callable tools so an agent can answer operational questions about tickets, pipeline health, runs, incidents, and governed metrics with every claim cited to the exact tool call it came from. All writes are proposal-only, requiring human approval through a gated apply path that logs each step for audit.
    MIT
  • A
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    Not graded
    quality
    C
    maintenance
    Enables named, authenticated callers to run read-only introspection of a Google Cloud project and to read and write per-tenant notes that stay isolated between callers. Every request is scope-checked against a reviewed policy, rate-limited per caller, and audited without ever logging argument values.
    MIT
  • A
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    Not graded
    quality
    C
    maintenance
    A standalone Python/FastAPI server that implements the Model Context Protocol (MCP) for the OPTIX threat intelligence platform. It exposes 26 analyst-friendly tools that AI assistants and programmatic consumers can use to query threat feeds, search documents and indicators, manage watchlists, triage IOCs, generate detection rules, trigger AI research, and produce intelligence reports.
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables AI clients like Claude to triage, investigate, and operate Icinga installations through natural language, integrating with Icinga's REST APIs and providing deep awareness of monitoring plugins and historical performance data.
    5
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  • A
    license
    Not graded
    quality
    D
    maintenance
    Enables automated review and analysis of network security controls (AWS Security Groups, Network ACLs) using natural language queries. Supports NSC configuration reviews, network segmentation testing, and identifying security gaps through conversational LLM interaction.
    MIT
  • F
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    Not graded
    quality
    D
    maintenance
    A comprehensive MCP server providing advanced Redmine project analytics, web automation via Playwright, and iTunes music integration. It enables detailed tracking of sprint metrics, bug counts, and team performance through a token-optimized tool architecture.
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