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Showcases the technology vision, core strengths, and application scenarios of the Agent to Agent - Multi-agent Collaboration Protocol.
Connects AI assistants to arXiv, enabling search, analysis, and download of research papers within AI workflows.
Provides an MCP-compatible Flask server for Claude Desktop, leveraging the EduChain library to dynamically generate diverse educational content.
Connects and manages Notion content through large language models like Claude.
Provides a comprehensive RESTful API for bidirectional Mi'kmaq language dictionary lookups and translations.
Creates a local database of indexed and optimized technical documentation, enabling AI agents to efficiently query and retrieve information from web sources and local files.
Provides AI coding assistants with searchable access to official PDFDancer SDK documentation.
Provides seamless access to Skolverket's open APIs, integrating educational data for AI assistants and large language models.
Searches Model Context Protocol documentation using advanced ranking algorithms and provides full document content retrieval.
Enables semantic search over local PDF, Excel, SQL, Markdown, and HTML files using a vector database and AI embeddings.
Provides an Obsidian-like memory vault for AI assistants, enabling shared, structured, and back-linked markdown notes.
Provides persistent semantic memory for Claude Code, allowing decisions, solutions, and context to survive across development sessions.
Facilitate the retrieval of academic papers by DOI, arXiv ID, or URL from multiple sources with intelligent routing.
Integrates GitHub repository and package intelligence directly into Claude Desktop for streamlined code research and auditing.
Provides AI agents with structured access to markdown documents for precise, outline-guided information retrieval.
Empowers Claude with a comprehensive toolkit for authoring, validating, and linting SailPoint ISC transforms from natural language descriptions.
Automates scientific research by enabling AI clients to search ArXiv, ingest papers via IBM DocLing, and provide RAG-powered Q&A over a personal library of academic documents.
Transforms the NCERT/CBSE curriculum (Grades 7–12) into structured, queryable data via an MCP server and REST API.
Empowers AI assistants with deep codebase understanding, advanced analysis tools, and persistent memory across conversations.
Establishes a shared, persistent knowledge network for AI agents, allowing them to retain and exchange information across sessions and teams.
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