Packages

Provides an MCP server (FastMCP) and supporting FastAPI “Store API” for importing, storing (PostgreSQL + pgvector), and semantic-searching German legal texts (scraped/parsed from gesetze-im-internet.de). Includes a CLI for importing/querying and an MCP tool set for AI assistants to search and retrieve legal sections.

An open-source desktop application that serves as an MCP host, connecting any LLM (ChatGPT, Anthropic, Ollama, OpenAI-compatible) with MCP servers through a unified chat interface. Supports stdio and SSE MCP transports, includes built-in tools (Fetch, File Manager, Bash), offers 24+ language support, and provides an installer agent for automatic MCP server configuration. Available in Electron and Tauri variants across Windows, macOS, and Linux.

GhidrAssistMCP is a Ghidra extension that runs an MCP (Model Context Protocol) server, exposing a set of reverse-engineering tools, static program resources, and pre-built prompts so external AI assistants/tools can query and modify analysis state inside Ghidra (e.g., functions, strings, types, comments, renaming, structure operations).

Provides a Python-based Model Context Protocol (MCP) server that connects to one or more Jenkins instances to diagnose build failures, explore sub-build hierarchies, trigger builds, discover job parameters, and extract/search log content. It supports configurable diagnostic rules and optionally semantic/vector search for cross-build similarity.

Provides an MCP server that exposes Java/JMX management operations through the Jolokia JMX-over-HTTP bridge. Supports two modes: a standalone MCP server (connects to an existing Jolokia agent) and a JVM-agent MCP server (drops in as a JVM agent and opens an HTTP MCP endpoint).

An MCP (Model Context Protocol) server that provides Kubernetes diagnostic tooling for pods and clusters, including pod health analysis, cluster health overviews, pod log analysis for error patterns, and workload recommendations, backed by Kubernetes API access via in-cluster service account or kubeconfig.

Provides a Go-based Model Context Protocol (MCP) server that exposes Kubernetes resources as MCP tools (e.g., Pod/Deployment/Service/etc.) and also offers a generic custom-resource tool for CRDs. Operations are executed against a Kubernetes cluster using a provided kubeconfig.

MCP-Bench is an evaluation framework that benchmarks tool-using LLM agents on complex tasks using the Model Context Protocol (MCP). It orchestrates discovery/connection to multiple MCP servers, runs benchmark tasks (single- and multi-server settings), and evaluates outputs (including LLM-as-judge).

Provides a deployable MCP Gateway (reverse-proxy via Nginx) and a MCP Registry/UI to register and govern MCP servers, discover tools (including schemas via ListTools), and route MCP client traffic to enabled backend MCP servers using per-service URL paths. Includes health checks and real-time UI updates via WebSockets.

mcphub is a Python package/CLI that helps developers configure, install/run, and connect MCP (Model Context Protocol) servers into AI applications. It supports stdio-based MCP connections and optionally an SSE-based mode (via a `mcphub run ... --sse` supergateway-style setup), provides a JSON `.mcphub.json` configuration format, and includes adapters to integrate MCP tools with frameworks such as OpenAI Agents, LangChain, and Autogen.

An MCP (Model Context Protocol) server that lets an AI assistant trigger cross-platform desktop notifications on behalf of the user. It exposes tools such as basic/urgent notifications, notifications with icons and timeouts, and a richer fully customizable notification.

Provides an MCP server that fronts a Prometheus instance so an MCP-capable client (e.g., Cursor) can query Prometheus for metrics, labels, targets, rules, and alert information using an API key for access control.

Qurio is a self-hosted ingestion and retrieval (RAG) engine for AI coding assistants. It crawls or ingests local documents (e.g., web pages, PDFs, Markdown), chunks content structurally, embeds it (Gemini for embeddings), stores vectors/metadata in Weaviate/PostgreSQL, and exposes retrieval to agents via an MCP server over a JSON-RPC 2.0 endpoint.

Sample Python project showing how to build an agentic web automation assistant using AWS Bedrock (Claude and Amazon Nova) with tool use, human-in-the-loop interruptions, and vision (screenshots). Includes progressive steps culminating in an MCP (Model Context Protocol) client/server refactor and conversation history/token-management enhancements.

Solana MCP server that exposes a set of Solana JSON-RPC-like capabilities (accounts, blocks, transactions, tokens, cluster/system info, staking/governance) to an MCP-capable client (described as working through Cline) using Rust.

Provides a local, read-only MCP server that exposes Square data as queryable tables/columns via a wrapped CData JDBC Driver. It runs as an MCP stdio server intended for use with local MCP clients (e.g., Claude Desktop) to retrieve live Square data without requiring users to write SQL.

zin-mcp-client is a local CLI/Web UI MCP client that launches and proxies STDIO-based MCP servers (via a local MCP config), and connects them to local LLMs running on Ollama to perform tool-calling through the MCP servers. It also provides an MCP proxy for integration with Open WebUI.

Arduino MCP server (Go) that exposes MCP tools to list connected Arduino boards and compile/upload Arduino sketches, intended to be run locally via stdio (e.g., from VS Code MCP settings).

Provides a headless MCP server for Binary Ninja, exposing a large tool surface (analysis, disassembly/IL, patching, types/metadata, undo/redo, memory operations, scripting, and more) over stdio and TCP transports, including a fake backend mode for CI/dev without a Binary Ninja license.

A Python utility for converting Compiled HTML Help (CHM) files to Markdown format, specifically optimized for Revit API documentation. This tool extracts HTML files from CHM documents and converts them to well-formatted Markdown files, making technical documentation more accessible, version control friendly, and AI-readable.