A minimal MCP server template that offers calculator tools, resources, and prompts for learning NitroStack fundamentals, built with TypeScript and Zod.
Sandboxed Python execution with automatic dependency management. Executes Python scripts in isolated environments (bubblewrap or Docker) with PEP 723 inline dependencies, preventing host pollution.
Code-mode MCP server (docs_search + execute_code two-tool surface) backed by a unified capability manifest across three sandbox kernels (in-process node:vm, WASM via QuickJS / Pyodide / Wasmtime, and remote microVM via E2B / Cloudflare Sandbox). At N=30 tools the bootstrap-context cost drops to 13.6% of direct tool-use. Apache-2.0.
A minimal MCP server that enables HTTP requests with any method, headers, and body types, supporting large responses through chunked transfers with disk-backed caching.
Enables AI assistants to render 3D models from OpenSCAD code, generating single views or multiple perspectives with full camera control. Supports animations, custom parameters, and returns base64-encoded PNG images for seamless integration.
MCP server for managing ParchMark notes via Claude Code/Desktop. Provides tools to list, get, create, update, and delete notes through natural language.
An MCP server that reads a clinical note and returns ranked ICD-10-CM diagnosis code candidates with clinical reasoning, validated against official CMS coding constraints.
Multi-provider image generation MCP server that enables image generation from Claude Desktop, Claude Code, or any MCP client using OpenAI, Google Gemini, Stable Diffusion, or a placeholder provider.
A multi-domain MCP server that exposes a small, stable surface of three tools (list_domains, list_domain_tools, call_domain_tool) to gate access to various tool domains like Plane and BloodBank, preventing agent schema overload.
A read-only MCP server that provides AI assistants with structured access to SonarQube projects, issues, metrics, and rules. It enables safe analysis of code quality and security findings through a set of validated, safety-first tools.
Analyzes source code structure across multiple languages using tree-sitter, extracting classes, functions, methods, and metadata with precise line numbers for efficient codebase exploration and AI-assisted development.
Enables AI assistants and developers to analyze code for language-specific best practices and idiomatic patterns across programming languages, CI automation, and configuration formats.