"Local RAG system for providing documentation to a large language model (LLM)" matching MCP servers:

Provides AI agents with a toolset to query model inventories, trace dependencies, and analyze the impact of changes across machine learning models and data pipelines.

Enables language models to receive external randomness so the same prompt can produce different answers while still following the user's request.

Exposes a RAG document-search API as MCP tools (rag_health, rag_ingest, rag_query), enabling agents to index and search markdown documents with cited results through natural language.

Unified MCP server for managing local model runtimes (Ollama, LM Studio, etc.), enabling provider-agnostic discovery, lifecycle management, hardware-fit checks, and delegated inference.

Monitors brand mentions, citations, sentiment, competitor share of voice, and GEO performance across AI search engines.

MCP server that routes AI chat requests to the best available provider (Anthropic, OpenAI, Groq, etc.) with automatic fallback, budget caps, and model-tier routing.

A Model Context Protocol server for indexing and searching local document collections with hybrid BM25+vector search, optimized for Korean and English, supporting DOCX, PDF, audio, and more.

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

Enables product enrichment with sentiment analysis, category mapping, and attribute extraction; provides tools to fetch top products and cluster summaries.

Implementation of an MCP server for the RAG Web Browser Actor. This Actor serves as a web browser for large language models (LLMs) and RAG pipelines, similar to a web search in ChatGPT.

Enables AI assistants to query PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB databases through MCP with read-only, audited access.

Enables local knowledge base management with retrieval-augmented generation (RAG), providing semantic search, document reading, listing, and Q&A via MCP tools and REST endpoints, all running locally without cloud dependencies.

MCP server to mock OpenAI, Anthropic, and Google Gemini responses for testing AI integrations without real API keys or token spend.

Enables AI agents to compose MCP tool calls into Unix-style pipelines, executing complex workflows server-side and returning only the final result.

Enables an AI assistant to build and revise an explicit shared world model, then reason, create, narrate, remember, and give writing feedback through inspectable processes, concepts, and cuts. Supports general-purpose modeling, storytelling, agent/user memory, and ideation over one engine and graph while keeping estimates, interpretations, and observations distinct.

Enables Claude Code to consult external LLMs (GPT, Gemini) through multi-turn sessions for second opinions, parallel consultations, and web-grounded research.

An MCP server that provides AI assistants with access to Vonage API documentation, code snippets, tutorials, and troubleshooting resources.

Give your AI agents instant access to Keboola API documentation. Search endpoints, get parameter details, and generate curl examples—all through MCP tools.

Minimal local RAG stack with an MCP server that provides document search for any agent.

Enables querying a Neo4j knowledge graph about Wroclaw University of Science and Technology using natural language. Converts user questions into Cypher queries and retrieves contextual information through an intelligent RAG pipeline with LLM-powered query routing.