"How to Work with Excel and Word Documents" matching MCP servers:

An MCP server that adds engineering discipline to AI-assisted development, enforcing evidence-gated TDD, security review, backup strategy, and deployment generation to turn AI-generated code into production-ready software.

Converts Jenkins declarative pipelines into GitHub Actions workflow YAML files, facilitating migration from Jenkins to GitHub Actions CI/CD.

Monitor failing GitHub Actions workflows, auto-fix them with OpenCode, and retry the run — driven by a workflow_run webhook, coordinated through an MCP server.

This MCP server publishes commit-bound proof summaries to a specified HTTPS webhook, featuring fail-closed writes and a clear authority boundary for safe external actions.

Enables interaction with Azure DevOps services through Personal Access Token authentication. Provides 75 tools for managing work items, builds, repositories, pull requests, and other DevOps operations via both CLI and HTTP API interfaces.

Publishes what an AI made — a page, a markdown or PDF doc, or a whole site — to a live HTTPS link. Runs locally over stdio from the @drop2run/mcp npm package, or hosted at https://mcp.dropto.run/mcp for chats that connect by URL.

Enables AI agents and external systems to programmatically trigger and monitor Jenkins jobs, retrieve build status and logs via MCP standards.

Enables AI agents to deploy static files and get live HTTPS URLs instantly, with support for custom domains and data residency.

Checks npm and PyPI packages for outdated versions, deprecation status, and breaking changes with cited sources, enabling AI agents to verify dependency freshness.

Enables AI clients to assess infrastructure-as-code deployment risk, generate incident response runbooks, review CI/CD delivery readiness, and calculate SLO error budgets through structured advisory tools. Runs locally over stdio without cloud credentials or external API calls, returning guidance for platform work, pull request review, and production readiness checks.

Provides disposable email inboxes for AI agents to automatically receive and extract OTPs and magic links, enabling seamless email verification during autonomous workflows.

A Model Context Protocol server that enables AI assistants to interact with Azure DevOps services, allowing users to query work items with plans to support creating/updating items, managing pipelines, handling pull requests, and administering sprints and branch policies.

Developers are constantly feeding context files to AI tools like Cursor, Windsurf, Copilot Workspace, and Claude Code. If your agent doesn't understand your real database schema, it writes code that fails in production. LintBase acts as the bridge. It connects directly to your database, reads the ground truth of your live documents, and generates structured context optimized for AI. 🤖 Stops AI

Baron gives an AI coding agent one normalized contract for work orchestration: create and transition issues, cut branches, open and merge pull requests, and read CI and deployment status across Azure DevOps, GitHub and Slack.

Provides read-only access to Azure DevOps using your browser session, no PAT required. Enables browsing projects, repos, work items, pull requests, and feeds through MCP tools.

A Model Context Protocol server that lets development agents work with Odoo.sh from their development environment, listing branches, watching builds, and streaming logs. It sits on top of the odouche Python library's public interface.

Enables interaction with Rundeck instances to list jobs, execute jobs with options, and retrieve execution status and logs through natural language commands.

Enables AI assistants to query and manage Azure DevOps projects, boards, work items, repos, pull requests, and pipelines while enforcing governance boundaries such as access modes, project scoping, protected projects, delete gating, and typed confirmations.

A local MCP server that connects Claude Desktop, Claude Code, and Claude Chat to your Azure DevOps organization, enabling querying work items, reviewing pull requests, triggering pipelines, searching code, managing test plans, and updating wikis using plain language.

Enables natural language interactions with CircleCI through Model Context Protocol, allowing users to retrieve build logs, find flaky tests, check pipeline statuses, and manage workflows without leaving their IDE.