The Wikipedia MCP Server is a Model Context Protocol (MCP) server that provides real-time access to Wikipedia information for Large Language Models (LLMs). It allows AI assistants to retrieve accurate and up-to-date information from Wikipedia to enhance their responses. The server offers features such as searching Wikipedia, retrieving article content, getting article summaries, extracting specific sections, discovering links within articles, finding related topics, supporting multiple languages and country codes, optional caching for improved performance, and compatibility with Google ADK agents and other AI frameworks. Users can install the server using pipx, Smithery, PyPI, virtual environment, or from source. The server can be run with various options for transport protocol, language, country/locale, caching, access token, and more. It also supports Docker and Kubernetes deployment. The server provides MCP tools for interacting with Wikipedia, such as searching articles, getting article content, summaries, sections, links, coordinates, related topics, and extracting key facts. It also supports country/locale codes and language variants for languages like Chinese, Serbian, Kurdish, and Norwegian. The server includes example prompts for querying Wikipedia and provides MCP resources for interacting with Wikipedia through MCP endpoints. The project structure includes main packages, API implementation, core functionality, utility functions, and a comprehensive test suite for reliability and functionality testing.

Memento MCP is a scalable, high-performance knowledge graph memory system designed for LLMs. It offers semantic retrieval, contextual recall, and temporal awareness to any LLM client supporting the model context protocol. The system is built on core concepts like entities and relations, utilizing Neo4j as its storage backend for unified graph and vector search capabilities. With advanced features such as semantic search, temporal awareness, confidence decay, and rich metadata support, Memento MCP provides a robust solution for managing knowledge graphs efficiently and effectively.

mcp-omnisearch is a Model Context Protocol (MCP) server that acts as a unified gateway to multiple search providers and AI tools. It integrates Tavily, Perplexity, Kagi, Jina AI, Brave, Exa AI, and Firecrawl to offer a wide range of search, AI response, content processing, and enhancement features through a single interface. The server provides powerful search capabilities, AI response generation, content extraction, summarization, web scraping, structured data extraction, and more. It is designed to work flexibly with the API keys available, enabling users to activate only the providers they have keys for and easily add more as needed.

Zotero MCP seamlessly connects your Zotero research library with AI assistants like ChatGPT and Claude via the Model Context Protocol. It offers AI-powered semantic search, access to library content, PDF annotation extraction, and easy updates. Users can search their library, analyze citations, and get summaries, making it ideal for research tasks. The tool supports multiple embedding models, intelligent search results, and flexible access methods for both local and remote collaboration. With advanced features like semantic search and PDF annotation extraction, Zotero MCP enhances research efficiency and organization.

ck (seek) is a semantic grep tool that finds code by meaning, not just keywords. It replaces traditional grep by understanding the user's search intent. It allows users to search for code based on concepts like 'error handling' and retrieves relevant code even if the exact keywords are not present. ck offers semantic search, drop-in grep compatibility, hybrid search combining keyword precision with semantic understanding, agent-friendly output in JSONL format, smart file filtering, and various advanced features. It supports multiple search modes, relevance scoring, top-K results, and smart exclusions. Users can index projects for semantic search, choose embedding models, and search specific files or directories. The tool is designed to improve code search efficiency and accuracy for developers and AI agents.

Orra is a tool for building production-ready multi-agent applications that handle complex real-world interactions. It coordinates tasks across existing stack, agents, and tools run as services using intelligent reasoning. With features like smart pre-evaluated execution plans, domain grounding, durable execution, and automatic service health monitoring, Orra enables users to go fast with tools as services and revert state to handle failures. It provides real-time status tracking and webhook result delivery, making it ideal for developers looking to move beyond simple crews and agents.

The mcp-documentation-server is a lightweight server application designed to serve documentation files for projects. It provides a simple and efficient way to host and access project documentation, making it easy for team members and stakeholders to find and reference important information. The server supports various file formats, such as markdown and HTML, and allows for easy navigation through the documentation. With mcp-documentation-server, teams can streamline their documentation process and ensure that project information is easily accessible to all involved parties.

The docs-mcp-server repository contains the server-side code for the documentation management system. It provides functionalities for managing, storing, and retrieving documentation files. Users can upload, update, and delete documents through the server. The server also supports user authentication and authorization to ensure secure access to the documentation system. Additionally, the server includes APIs for integrating with other systems and tools, making it a versatile solution for managing documentation in various projects and organizations.

LEANN is an innovative vector database that democratizes personal AI, transforming your laptop into a powerful RAG system that can index and search through millions of documents using 97% less storage than traditional solutions without accuracy loss. It achieves this through graph-based selective recomputation and high-degree preserving pruning, computing embeddings on-demand instead of storing them all. LEANN allows semantic search of file system, emails, browser history, chat history, codebase, or external knowledge bases on your laptop with zero cloud costs and complete privacy. It is a drop-in semantic search MCP service fully compatible with Claude Code, enabling intelligent retrieval without changing your workflow.

OpenEdison is a secure MCP control panel that connects AI to data/software with additional security controls to reduce data exfiltration risks. It helps address the lethal trifecta problem by providing visibility, monitoring potential threats, and alerting on data interactions. The tool offers features like data leak monitoring, controlled execution, easy configuration, visibility into agent interactions, a simple API, and Docker support. It integrates with LangGraph, LangChain, and plain Python agents for observability and policy enforcement. OpenEdison helps gain observability, control, and policy enforcement for AI interactions with systems of records, existing company software, and data to reduce risks of AI-caused data leakage.

TunaCode CLI is an AI-powered coding assistant that provides a command-line interface for developers to enhance their coding experience. It offers features like model selection, parallel execution for faster file operations, and various commands for code management. The tool aims to improve coding efficiency and provide a seamless coding environment for developers.

Roast is a convention-oriented framework for creating structured AI workflows maintained by the Augmented Engineering team at Shopify. It provides a structured, declarative approach to building AI workflows with convention over configuration, built-in tools for file operations, search, and AI interactions, Ruby integration for custom steps, shared context between steps, step customization with AI models and parameters, session replay, parallel execution, function caching, and extensive instrumentation for monitoring workflow execution, AI calls, and tool usage.

AgentPress is a collection of simple but powerful utilities that serve as building blocks for creating AI agents. It includes core components for managing threads, registering tools, processing responses, state management, and utilizing LLMs. The tool provides a modular architecture for handling messages, LLM API calls, response processing, tool execution, and results management. Users can easily set up the environment, create custom tools with OpenAPI or XML schema, and manage conversation threads with real-time interaction. AgentPress aims to be agnostic, simple, and flexible, allowing users to customize and extend functionalities as needed.

Probe is an AI-friendly, fully local, semantic code search tool designed to power the next generation of AI coding assistants. It combines the speed of ripgrep with the code-aware parsing of tree-sitter to deliver precise results with complete code blocks, making it perfect for large codebases and AI-driven development workflows. Probe supports various features like AI-friendly code extraction, fully local operation without external APIs, fast scanning of large codebases, accurate code structure parsing, re-rankers and NLP methods for better search results, multi-language support, interactive AI chat mode, and flexibility to run as a CLI tool, MCP server, or interactive AI chat.

Build your own conversation-based search with AI, a simple implementation with Node.js & Vue3. Live Demo Features: * Built-in support for LLM: OpenAI, Google, Lepton, Ollama(Free) * Built-in support for search engine: Bing, Sogou, Google, SearXNG(Free) * Customizable pretty UI interface * Support dark mode * Support mobile display * Support local LLM with Ollama * Support i18n * Support Continue Q&A with contexts.

This project provides a local, OpenAI-compatible text-to-speech (TTS) API using `edge-tts`. It emulates the OpenAI TTS endpoint (`/v1/audio/speech`), enabling users to generate speech from text with various voice options and playback speeds, just like the OpenAI API. `edge-tts` uses Microsoft Edge's online text-to-speech service, making it completely free. The project supports multiple audio formats, adjustable playback speed, and voice selection options, providing a flexible and customizable TTS solution for users.

The Wikipedia MCP Server is a Model Context Protocol (MCP) server that provides real-time access to Wikipedia information for Large Language Models (LLMs). It allows AI assistants to retrieve accurate and up-to-date information from Wikipedia to enhance their responses. The server offers features such as searching Wikipedia, retrieving article content, getting article summaries, extracting specific sections, discovering links within articles, finding related topics, supporting multiple languages and country codes, optional caching for improved performance, and compatibility with Google ADK agents and other AI frameworks. Users can install the server using pipx, Smithery, PyPI, virtual environment, or from source. The server can be run with various options for transport protocol, language, country/locale, caching, access token, and more. It also supports Docker and Kubernetes deployment. The server provides MCP tools for interacting with Wikipedia, such as searching articles, getting article content, summaries, sections, links, coordinates, related topics, and extracting key facts. It also supports country/locale codes and language variants for languages like Chinese, Serbian, Kurdish, and Norwegian. The server includes example prompts for querying Wikipedia and provides MCP resources for interacting with Wikipedia through MCP endpoints. The project structure includes main packages, API implementation, core functionality, utility functions, and a comprehensive test suite for reliability and functionality testing.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.