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    MCP server for multiple databases (PostgreSQL, MySQL, MariaDB, SQLite, MongoDB, Redis) with tools for schema inspection, querying, performance diagnostics, and safe write operations, featuring access modes, PII masking, and audit logging.
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    Provides Claude with read-only access to PC diagnostics including hardware specs, live performance metrics, disk usage, network checks, and more, enabling natural language queries about PC health and performance.
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    MIT
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    Enables AI assistants to search, analyze, and debug application API traffic captured by Tusk Drift, including HTTP requests, database queries, distributed traces, latency metrics, and error rates.
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    MIT
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    Enables agents to report and query performance metrics to build a community-driven quality database for MCP tools. This server helps agents discover and select the most reliable tools based on success rates and user-reported quality scores.
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    4
    MIT
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    A Model Context Protocol (MCP) server for PostgreSQL that provides safe, structured access to your database for AI assistants, enabling health checks, index tuning, lock analysis, and more.
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    MIT
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    Enables Claude to interact with Karma Alert dashboard to monitor and analyze Kubernetes alerts. Provides tools to check alert status, filter by cluster/severity, get detailed alert information, and analyze alert statistics and trends.
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    MIT
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    A Model Context Protocol server that enables retrieving and analyzing Sentry issues, including stacktraces and debugging information.
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    Enables interaction with Blacksmith CI analytics to query workflow runs, jobs, test results, and usage metrics. It provides detailed access to CI/CD data including job logs and billing information directly through Claude.
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    Enables interaction with Google Cloud Platform services through gcloud CLI, supporting operations like querying logs, checking Cloud Run status, managing secrets, executing Cloud SQL queries, and monitoring billing information.
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    The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.
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    MIT