"Generating Application System Architecture Schemes" matching MCP servers:

Local-first knowledge system for reasoning agents, exposing facts, evidence, documents, retrieval, and audit history through a thin stdio MCP server.

MCP server for a modular RAG system that enables natural language question answering over enterprise documents with intent-aware routing, adaptive retrieval, and citation-backed responses.

About Your task manager is the best agent memory you're not using. An agent-native context layer over your existing task app.

Pre-trade risk validation and position sizing for AI trading agents via G-formula and Iron Fist.

Provides AI clients with a persistent, readable and writable long-term memory layer over MCP, enabling creation, retrieval, tagging, updating, and deletion of memories across sessions.

A simple application demonstrating Model Context Protocol (MCP) integration with FastAPI and Streamlit, allowing users to interact with LLMs through a clean interface.

Enables AI applications to access and contextualize organizational knowledge sources including GitHub repositories and internal documentation through standardized MCP protocol integration. Features OAuth 2.1 authentication, vector-based semantic search, and optimized context chunking for enterprise development workflows.

Enables LLM clients to query local markdown engineering documentation by indexing files into ChromaDB with Ollama embeddings and exposing a search_internal_docs tool over stdio, all offline and privacy-first.

A vector search system that enables semantic retrieval of document chunks using MongoDB Atlas Vector Search and Voyage AI embeddings, allowing users to search documents by meaning rather than just keywords.

A Retrieval-Augmented Generation system that enables uploading, processing, and semantic search of PDF documents using vector embeddings and FAISS indexing for context-aware question answering.

An MCP-compatible RAG backend using LangGraph and FastAPI, enabling chaining of AI model logic with document context search.

Transforms code repositories and development documentation into a queryable Neo4j knowledge graph, enabling AI assistants to perform intelligent code analysis, dependency mapping, impact assessment, and automated documentation generation across 15+ programming languages.

Enables integration with financial transaction data through REST APIs, PostgreSQL databases, and document storage systems. Demonstrates agentic AI capabilities by connecting to Alpha Vantage API and managing financial data through natural language interactions.

Enables intelligent data analysis and querying of Snowflake databases through specialized AI agents. Features 20+ tools for data operations, lineage tracing, usage analysis, and performance optimization with multi-agent architecture.

Automates file system operations through natural language commands using a combination of LLM (Ollama), MCP tools, and a Python bridge agent.

Provides file system access for resume matching through JSON-RPC, enabling semantic search and workflow orchestration with LangGraph.

Enables AI coding assistants to query private academic paper collections via standard MCP tools, with hybrid retrieval, reranking, and inline citations.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Enables semantic search and natural language product recommendations using ChromaDB vector store and Azure OpenAI embeddings. Supports multi-filter search by category, brand, and price with 8 specialized MCP methods for intelligent product discovery.

An enterprise-grade AI processing platform that provides advanced code analysis, automated documentation generation, security scanning, and knowledge extraction capabilities through a scalable MCP architecture. Features iterative neural processing, support for 100+ programming languages, and real-time streaming updates.