"How to execute custom code in a Linux Docker container" matching MCP servers:

A self-hosted MCP server that exposes Jira and Confluence Cloud to MCP clients, enabling issue tracking, search, creation, and Confluence page management via natural language.

Dynamically fetches and exposes OpenAPI/Swagger documentation as tools for AI assistants, enabling them to explore and interact with any REST API.

A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.

Enables converting a public release-note URL into ordered upgrade tasks with source excerpts, anchors, confidence, and uncertainty, without executing release text or project code.

Connects to vibe-code-best-practices.vercel.app to index 817 tools across 185 sections and 10 domains, routes the right tool/Skill for your project need, and can recommend a full stack.

A Model Context Protocol server that converts Markdown documents to plain text with flexible options and dual protocol support.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

Converts Markdown files to Atlassian Document Format and publishes them to Confluence, with support for rendering Mermaid diagrams as PNG attachments.

Converts any OpenAPI/Swagger spec into an MCP server, exposing REST API endpoints as tools for Claude.

Enables converting Markdown text into clean HTML for headings, lists, code blocks, tables, links, and images, with optional full-document wrapping via x402 micropayments.

A minimal MCP server that auto-generates tools from your OpenAPI spec, optimized for Vercel.

Provides AI coding assistants with access to ITK Dev Docker documentation and project analysis tools, enabling template listing, project detection, and comparison.

An MCP server that provides static validation and documentation lookup for quantum computing libraries, currently supporting PennyLane. It allows users to verify quantum code syntax and method usage against official library documentation to ensure correct implementation.

Provides AI agents with a governed, three-layer project memory (guide, code facts, and knowledge) through namespaced MCP tools for code search, context compilation, impact analysis, and proposal-driven documentation updates.

Allows LLM tools like Claude Desktop and Cursor AI to access and summarize code files through a Model Context Protocol server, providing structured access to codebase content without manual copying.

Enables local conversion of PDFs to Markdown, with automatic OCR fallback for scanned pages, image extraction, and page-by-page structure preservation.

Searches Claude Code documentation via BM25 indexing, returning ranked snippets with category filtering. Enables fast, local search over official docs without full-document scans.

High-performance code understanding toolkit that enables batch reading of multiple files with dependency context, structural outline extraction with Java annotation awareness, and precise location of classes/methods across large codebases.

Facilitates searching and accessing programming resources across platforms like Stack Overflow, MDN, GitHub, npm, and PyPI, aiding LLMs in finding code examples and documentation.

Provides AI assistants with access to personal best practice skills and coding guidelines stored as markdown files in a GitHub repository, enabling retrieval and listing of custom development standards.