"documentation" matching MCP servers:

Provides AI assistants with access to Capawesome documentation and Cloud management tools, enabling app, build, and deployment operations via API token authentication.

AutomateLab n8n MCP helps AI agents generate and lint workflows, diagnose failed executions, and manage live n8n instances. Product and documentation: https://automatelab.tech/products/mcp/n8n-mcp/

Production-ready, zero-knowledge AI DevOps deployment, infrastructure provisioning, self-healing execution, and persistent documentation engine for VPS servers.

An MCP server that connects LLMs to CERN GitLab to discover and analyze High Energy Physics code, documentation, and analysis examples. It provides 14 tools for repository browsing, dependency parsing, and CI/CD configuration analysis.

Enables Claude to interact with R package development workflows, including CRAN metadata lookup, dependency checking, testing, documentation validation, and release automation.

Provides structured project management through phase-based workflows, enabling planning, execution tracking, compliance checking, and automated documentation for software development projects.

Enables AI assistants to interact with Coolify self-hosted instances for application deployment, management, and monitoring. Features 4 unified tools optimized for VS Code's limits, covering app management, environment configuration, system administration, and built-in documentation.

Multi-persona engineering mentor with 64 specialized AI personas that injects engineering standards before Claude reasons, transforming passive documentation into active collaborative guidance.

An MCP server that enables AI-powered IDEs to implement a structured development workflow from requirements gathering to code implementation, guiding users through goal collection, requirements specification, design documentation, task planning, and execution.

Enables intelligent access to AL-Go documentation, workflows, and domain expertise for Business Central development automation through specialized personas and cross-source semantic search.

Enables issue-to-code automation for GitHub and GitLab by connecting AI assistants (Claude Code, Gemini CLI, Codex) via MCP, with slash commands for issue analysis, planning, implementation, testing, documentation, and PR creation.

MCP server for project management automation, providing tools for project health, documentation, task management, security scanning, and CI/CD validation.

A DevOps-friendly template for building MCP servers with CI/CD, Docker support, and automatic documentation generation using fastmcp and FastAPI.

A secure, containerized MCP server that enables AI assistants to manage Terraform infrastructure using integrated Language Server Protocol (LSP) tools. It facilitates safe operations like initialization, validation, and planning while providing context-aware code completion and documentation.

Provides AI assistants instant access to GitLab's official documentation with intelligent search, section filtering, and full content retrieval across 2,494 documentation pages including CI/CD, API, admin, and development guides.

Enables intelligent Kubernetes deployments, automated documentation testing, organizational pattern management, and shared prompt libraries. Provides AI-powered recommendations based on cluster capabilities and automates DevOps workflows through conversational interfaces.

Enables natural language management of Expo and EAS projects, including SDK documentation, cloud builds, CI workflows, store submission, and TestFlight crash and feedback data.

Enables searching and retrieving official PipeCD documentation through full-text search and document retrieval. Clones PipeCD docs from GitHub and indexes Markdown files for quick access to titles and content.

Enables programmatic interaction with Expo/React Native projects and EAS (Expo Application Services) for managing builds, publishing OTA updates, submitting apps to stores, and accessing comprehensive documentation.

Exposes all PyPreset functionality to AI coding assistants via the Model Context Protocol. Enables scaffolding Python projects and augmenting existing ones with CI/CD, documentation, Docker, and more through natural language.