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"Improving Cursor Efficiency and Smart Features" matching MCP servers:

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    MCP server that wraps Cursor Enterprise API to enable AI agents to answer questions about team spending, usage, and model adoption.
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    MIT
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    Query Grafana logs, metrics, and dashboards from Cursor. Enables the AI to call your Grafana instance via tools without leaving the editor.
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    Apache 2.0
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    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.
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    Apache 2.0
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    Estimates the environmental footprint of your AI use — energy (kWh), miles driven, water used for cooling, and CO₂ — plus a prompt-efficiency score, working with any AI client by measuring token usage.
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    Provides intelligent analysis of token usage patterns and optimization recommendations to improve efficiency and reduce costs in Claude Code sessions. Offers real-time analysis, cost metrics, and actionable insights for better context window and tool usage optimization.
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    8 standalone MCP servers for SMBs — Guardrails (prompt injection, PII, secrets detection), FinOps (AI cost tracking and budgets), Observability (agent tracing), Smart Router (17+ models, 9 providers), Trust Score (governance scoring 0-100), Memory (persistent agent memory), ThinkSecure (runtime security + SOC2/GDPR audit), and A2A Lite (agent-to-agent protocol). 37 tools total, TNC credits
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    MIT
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    Compares AI coding tool spend before and after a migration -- Cursor, Claude Code, GitHub Copilot, OpenCode, Codex CLI -- via an MCP server, real numbers from each vendor's own API or local logs.
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    Apache 2.0
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    Exposes homelab and IT-ops tools to Claude, including system health monitoring, Grafana alert states, Docker container status, Loki logs, SMART disk health, and more.
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    An MCP server that provides offline searchable Microsoft 365 roadmap data and live Azure status, enabling AI agents to answer questions about upcoming features, roadmap changes, and Azure incidents.
    4
    MIT
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    An MCP server for AI-powered log analysis that enables parsing, searching, and debugging across nine log formats directly within Claude. It features automated error extraction, sensitive data scanning, and streaming support for large log files.
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    MIT
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    An MCP server that exposes GPU-accelerated anomaly detection to AI assistants via the Model Context Protocol. Provides two MCP tools: waveguard_scan (send training + test data in one call, returns per-sample anomaly scores and top explanatory features) and waveguard_health (check API and GPU status). Works on time series, JSON, numbers, text, and images — fully stateless.
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    MIT
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    MCP server for AWS infrastructure analysis. Connects to DynamoDB, Lambda, SQS, SNS, Secrets Manager, EventBridge, RDS, PostgreSQL, MongoDB, and CloudWatch — surfaces missing indexes, queue DLQs, Lambda config issues, IaC drift, and security findings for Claude Code and Cursor.
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    MIT
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    Enables AI assistants to query and analyze Alibaba Cloud SLS logs using natural language, supporting multiple log sources like Function Compute and ECS. It provides tools for searching logs, performing SQL analysis, and visualizing log distributions directly within Cursor or Claude.
    6
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    MIT
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    An MCP server for controlled server operations via BaoTa Panel, providing 50+ tools for system, site, database, files, cron, Docker, and network management with safety features like permission tiers, credential masking, and audit logs.
    2
    708
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    Apache 2.0
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    Enables AI agents to analyze ROS2 log files across multiple nodes by merging them into a unified timeline and providing smart query tools, without requiring ROS2 installation.
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    MIT
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    Provides system monitoring tools for VPS including CPU, memory, disk, network ping, top processes, and Docker status, with read-only architecture and security features.
    4
    MIT
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    Enables AI assistants to access full Datadog observability, including log search, APM trace filtering, smart sampling, and cross-correlation between logs, traces, and metrics.
    23
    735
    5
    Apache 2.0