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    MCP server connecting Claude/Cursor to Codex CLI, enabling code analysis via @ file references, multi-turn conversations, sandboxed edits, and structured change mode.
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    Integrates the Atlassian Rovo Dev CLI with the Model Context Protocol, allowing AI assistants to perform deep code analysis using Rovo Dev's large context capabilities. It enables features like repository-wide queries, file-specific analysis, and specialized coding modes through a standard MCP interface.
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    This is a powerful Model Context Protocol (MCP) server that integrates multiple AI coding agents—Anthropic Claude Code, OpenAI Codex, and Google Gemini—directly into your workflow. It enables seamless cross-provider analysis, leveraging Gemini's massive token window, Codex's specialized coding capabilities, and Claude's advanced reasoning.
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    Enables Claude Code to dispatch opencode CLI tasks as background processes with immediate task handle return, status polling, and result retrieval, avoiding tmux and log parsing issues.
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    Official implementation of Anthropic's 'think' tool that provides Claude with a dedicated space for structured reasoning, improving performance by up to 54% on complex tasks requiring multi-step problem solving.
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    Prevents coding agents from repeatedly attempting the same failed fix by tracking attempts and blocking further fixes until the agent uses its own web search tool.
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    Self-hosted coding agent that runs Claude Code or Codex in your own infrastructure and works from Slack. Exposes codebase investigation, file edits, branch and PR creation, allowlisted command execution, CI status and an audit log as MCP tools, keeping every credential behind the tool layer instead of handing it to the model.
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    This project aims to build a Claude Code MCP server and implement its associated tools (explain\_code, review\_code, fix\_code, edit\_code, test\_code, simulate\_command, your\_own\_query). The server is implemented using Node.js and the MCP SDK. It receives tool requests from clients via Stdio, dynamically
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    Enables coding agents to navigate and query source code by providing context, symbols, and call graph information through a graph index.
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    Supercharges AI coding agents with semantic code intelligence, providing pre-built knowledge graphs for surgical context, faster answers, and fewer tool calls.
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    MIT
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    Integrates Nautex AI's specification and planning tool with coding agents to manage requirements, tasks, and code generation in small, testable steps.
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    MIT