"Personalized Summary Service" matching MCP servers:

MCP bridge to a multimodal RAG service, enabling hybrid search and Q&A over documents with tools for knowledge base queries and health checks.

Exposes infocepo.com infrastructure (LLM, ChromaDB, S3, Docker registry, service discovery) as MCP tools for AI agents.

A service discovery and proxy for MCP servers that enables registration, discovery, and execution of tools on remote MCP servers. Uses vector-based similarity search through Alibaba Cloud services to intelligently route requests to appropriate MCP services.

Provides semantic memory and persistent storage for Claude, leveraging ChromaDB and sentence transformers for enhanced search and retrieval capabilities.

Provides intelligent, persistent memory for AI assistants with semantic search, natural language queries, and OAuth-based team collaboration, enabling context-aware conversations across multiple clients.

Enables agents to store text and metadata and later retrieve it by plain-language similarity search via Qdrant, exposing store and find tools. Embeddings are generated locally, so no external embedding service is required.

A cloud-based vector memory service that provides AI assistants with persistent storage, semantic search, and entity management via the Model Context Protocol. It features multi-tenant isolation and bidirectional synchronization with macOS and Google contacts and calendars.

A minimal RAG service that exposes a vector index for document retrieval via REST and MCP, allowing querying for relevant document chunks and returning a suggested LLM prompt.

Enables AI-assisted chiller troubleshooting by exposing MCP tools to retrieve operational data, alarms, telemetry, and service history from MongoDB Atlas, plus vector and hybrid search over knowledge bases.

Enables agents and terminal users to create, inspect, populate, query, update, and delete vector collections and documents through a Chroma-compatible HTTP service.

An independent memory service for AI agents that stores and retrieves facts with keyword and semantic search, context management, and a plugin-based architecture.

MCP server that connects Cursor IDE to Inky backend, enabling users to list and filter personalized coding rules stored in Pinecone.

Enables vector search and document retrieval, text extraction and chunking of files into Markdown, and private deep research through an integrated pipeline service.

This MCP server provides semantic document search and retrieval, enabling AI assistants to search documents, search categories, and retrieve category hierarchies using the Model Context Protocol.

Provides a single self-hosted memory service that any AI assistant can write to and recall from over MCP or REST, storing episodic, semantic, procedural, reflective and session memories in one shared store. Enables agents to remember events as they happen and receive a ranked, token-budgeted brief of relevant history, counts, lessons and procedures before they act.

Personal memory service using semantic vector retrieval and knowledge graphs to persist long-term context, supporting memory evolution and conflict labeling for the same entity.

Enables AI agents to search through OpenShift service notification logs using semantic search powered by ChromaDB and sentence transformers, helping find relevant notifications based on problem descriptions with metadata enrichment and variable interpolation support.

Connects Obsidian Markdown notes as searchable knowledge chunks via MCP tools, enabling semantic vector search and CRUD operations through a layered service architecture.

Enables character management and semantic search for the Auto-Movie application through WebSocket communication. Supports creating characters with personality/appearance descriptions and finding similar characters using natural language queries with embedding-based similarity matching.

An enhanced memory repository MCP service that builds and queries temporally-aware knowledge graphs from user interactions and data. Features asynchronous parallel processing, task management, broader AI model compatibility, and a comprehensive visual management interface.

A personal career-coach memory service that ingests coaching logs, embeds them for semantic search, and exposes the memory to Claude clients via MCP, with a Next.js dashboard for browsing.

Multi-modal RAG service exposing a REST API and MCP server for document indexing and knowledge-base querying, powered by RAGAnything and LightRAG.

A clean architecture backend API that registers and semantically searches data using vector embeddings, acting as an MCP context provider to connect AI models to external data.

Enables searching through vectorized Cursor IDE chat history via a FastAPI service powered by LanceDB and Ollama.

Enables AI-powered document analysis and querying for project documentation using vector embeddings stored in Redis. Supports document upload, context-aware Q\&A, automatic test case generation, and requirements traceability through OpenAI integration.

Enables Claude Desktop to search private documents using Azure AI Search and perform web searches with Bing, providing AI-enhanced results with source citations through Azure AI Agent Service or direct Azure AI Search integration.

Tracks learning progress and generates personalized daily learning insights using RAG to fetch relevant content from blogs, RSS feeds, and Reddit based on your current topics and learning goals.