A simple Model Context Protocol server that provides a hello world greeting tool, serving as a boilerplate template for quickly creating new MCP servers.
Provides comprehensive code quality analysis with quantitative metrics, historical trends, and refactoring risk prediction for C#, Python, and TypeScript codebases.
An MCP server that performs comprehensive health checks on project dependencies for JavaScript and Python projects, detecting outdated packages and fetching changelogs.
A minimal reference implementation of an MCP server that responds with "Hello, World" via Streamable HTTP. Serves as a baseline for integration testing and MCP client development with production-ready features including health checks, metrics, and containerized deployment.
Temporal knowledge graph for codebases that captures decision traces, links test failures to code changes, learns co-edit patterns, predicts regression risk, and enforces learned constraints at the edit boundary via a PreToolUse hook.
Monitors and analyzes GitHub repository health by detecting stale branches, old pull requests, unresponsive issues, and security alerts. Integrates with MCP-compatible AI assistants and automation tools.
A minimal Model Context Protocol server in TypeScript that demonstrates MCP-compliant resources and tools for LLMs, featuring simple resources and a basic tool that echoes messages or returns greetings.
Enables scanning projects for dependency vulnerabilities, secrets, license conflicts, code quality, and git health, returning a 0-100 health score with actionable suggestions.
A minimal, dependency-free MCP server demonstrating the 2026-07-28 protocol with a toy example of sending a person to check something in the physical world and returning structured evidence, ideal for learning the protocol layer.
MCP server for the Mamba Labs GitHub Organization Signal Scanner actor on Apify. Resolve a company domain to its GitHub organization with repo, language and activity signals.
Provides read-only repository health scanning tools for drift detection, module reachability, prompt bloat, evidence calibration, and registration completeness, enabling agents to diagnose repositories via MCP.
Provides audit_plugin_health and prepare_semantic_review tools for deterministic inspection of Codex plugins and Agent Skills, generating evidence-backed reports without executing or transmitting target code.
Monitors and analyzes dependency health in Node.js projects, providing tools to check outdated packages, vulnerabilities, licenses, and suggest safe upgrades.
Integrates live Screeps game data into AI development workflows, providing comprehensive tools for room operations, market analysis, and user management. It enables AI agents to monitor game status and interact with the game world through the Model Context Protocol.