"Slack Bot with RAG Document Retrieval and Automated Response System" matching MCP servers:

Enables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.

Enables AI agents to parse PDFs and search local document context through multimodal extraction and vector-based retrieval, all exposed as MCP tools.

Provides AI agents with multi-format document indexing, hybrid dense and sparse search with reranking, and relational SQL querying over extracted tables.

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.

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

An MCP server that retrieves relevant PDF chunks via local embeddings and returns them to IDE agents (Cursor, Kiro, Claude Code) for answer generation.

Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.

A black-box flight recorder for RAG retrieval inside MCP agents. Logs what chunks the model saw, scores, sources, and rankings - so you can audit, replay, and diff retrieval runs after the fact.

Enables local semantic search over PDF, DOCX, PPTX, and EPUB documents by converting them to Markdown, indexing them in ChromaDB, and retrieving complete structure-aware sections with tables and equations.

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.

MCP server enabling Claude Desktop to answer questions from local Word and PDF documents by searching a vector index built from their contents.

Bridges AI agents with watsonx.data document libraries, enabling natural language queries and intelligent retrieval from document libraries.

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.

Enables deterministic auditing of RAG retrieval faithfulness and hallucination scores, providing structured JSON output for AI agents and MCP-compliant clients.

Enables semantic search and question-answering over uploaded documents using vector embeddings and Google AI. Supports document organization with tags, section-aware queries, and hierarchical markdown structure preservation.

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.

The MCP server provides tools for entity extraction, query refinement, and relevance checking to enhance RAG applications by integrating with OpenAI and Gemini.

A RAG-based knowledge base system supporting document processing, semantic search, and intelligent Q\&A with multiple AI model integrations.

Provides AI agents with comprehensive document parsing capabilities including PDF text extraction, OCR, HTML-to-markdown conversion, table extraction, and summarization, optimized for agent workflows.

Enables structured team communication for Claude Code agents through Slack-like channels and direct messages. Supports project isolation, subscription management, and agent notes for sophisticated multi-agent collaboration workflows.