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"How to Save a DOCX File" matching MCP servers:

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    Provides over 1,000 creative ways to decline requests across four categories (polite, humorous, professional, and creative). The MCP server wraps a REST API to help users craft professional rejections through natural language interactions.
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    117
    MIT
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    An MCP server that provides multiple file conversion tools for AI agents, supporting various document and image format conversions including DOCX to PDF, PDF to DOCX, image conversions, Excel to CSV, HTML to PDF, and Markdown to PDF.
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    MIT
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    Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.
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    Apache 2.0
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    A "Virtual DOM" for Microsoft Word enabling AI to safely read, redline, and sanitize DOCX contracts without breaking formatting.
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    MIT
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    A simple MCP server that provides a basic greeting tool and serves as a starter template for AWS Lambda deployment. Demonstrates how to build and deploy MCP servers with both local development and cloud deployment capabilities.
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    1
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    MIT
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    Persistent, consensus-validated institutional memory for AI agents. Gives LLMs real memory that survives across sessions - validated through BFT consensus, not just dumped to a file.
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    Apache 2.0
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    Provides intelligent file system operations with advanced features including code analysis and modification across multiple languages, version control (Git/GitHub), file compression, encryption, semantic search, batch operations, and secure shell command execution.
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    MIT
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    MCP server to give new instructions to agent while its working. It uses the get_feedback tool to collect your input from the feedback.md file in the workspace, which is sent back to the agent when you save.
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    MIT
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    One-pass agentic inbox triage as an MCP server: fetch unread Gmail → classify (action_needed/fyi/newsletter/noise) → summarize → extract tasks → draft replies as Gmail DRAFTS (never sends) → flag calendar → write a triage report. Four stdio tools (fetch_emails, save_gmail_draft, append_tasks, write_report); the host is the LLM, so it runs keyless in Claude Code. Gmail scopes: readonly + compose
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    MIT
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    An MCP server that enables AI agents to retrieve detailed GitHub Pull Request information using git commit hashes, branch names, or PR numbers. It automatically detects repositories and extracts comprehensive PR data including descriptions, labels, and reviews via the GitHub CLI.
    Last updated
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    ISC
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    Manage Job using MCP: Manage Job, Candidates, Resumes, Salaries all within this one MCP tools It can solve problems like: You have 50 resumes to screen. Your AI assistant can reason about candidates, but it can't: Read PDFs/DOCX — The AI can't open binary files Extract structured data — Copy-pasting loses formatting, metrics, and context Compare at scale — No consistent scoring across candida
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    MIT