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"namespace:io.github.nousulabs-debug" matching MCP servers:

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    An intelligent debugging assistant that automates the debugging process by analyzing bugs, injecting HTTP-based debug logs into code across multiple environments (browser, Node.js, mobile, etc.), and iteratively fixing issues based on real-time feedback.
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    MIT
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    Enables AI assistants to perform interactive Python debugging with breakpoints, step execution, and variable inspection using the Debug Adapter Protocol (DAP) through an MCP server interface.
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    An MCP server and VS Code extension that enables AI clients to interactively debug code using breakpoints, execution control, and state inspection. It is language-agnostic and works with any debugger that supports VS Code's launch.json configurations.
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    Enables AI-powered debugging through structured logging with pattern recognition and intelligent analysis. Allows applications to write structured logs and receive actionable debugging insights based on error patterns and frequency analysis.
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    A debugging gateway for distributed systems that provides 17 tools for interacting with AWS CloudWatch, Step Functions, LangSmith, and Jira. It enables efficient log analysis, workflow tracing, and ticket management while significantly reducing token usage through a single-interface discovery pattern.
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    MCP server for broken link detection, monitoring, and AI-powered fix suggestions. Scans URLs or sitemaps, estimates SEO and revenue impact, and returns actionable remediation steps. Built with FastMCP 3.x.
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    MIT
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    Mandatory human approval gate for autonomous AI agents. Before any critical, irreversible, or financially significant action executes — file deletion, production deployment, financial transaction — the agent calls oracle_validate. A human receives a real-time Telegram notification with full context and taps Approve or Reject. The agent waits for the decision before proceeding.
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    MCP servers for quantitative trading: live trading signals, market data, NAV history, portfolio management with dual-track (AUTO/MANUAL) performance comparison. Supports both stdio and Streamable HTTP transport.
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    A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK, with example tools (calculator, greeting) and resources (server info) to help developers get started.
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    MIT
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    Connects AI coding assistants to Gaffer test history and coverage data to analyze project health, debug failures, and identify untested code areas. It enables tools to track test stability, cluster failures by root cause, and monitor code coverage trends across projects.
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