"Agentic RAG: Understanding or Exploring Its Meaning and Applications" matching MCP servers:

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.

Provides live wait times and office details for all 60 Oregon DMV field offices by scraping the ODOT website, with no authentication required.

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.

Read and write email through the Atomic Mail from an AI agent. Handles proof-of-work authentication and JMAP so the agent thinks in JMAP method calls. Use when the user asks to register an email inbox, list mailboxes, fetch or send email.

Enables AI agents to search, describe, and call paid data services from the Pocket Network agentic marketplace, paying per request in USDC via x402.

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.

Exposes an EVM wallet to AI agents via MCP, using AWS KMS for HSM-backed signing and a deny-by-default policy engine for secure DeFi transaction approvals.

A local MCP gateway that allows AI clients to manage and interact with multiple MCP servers through a single connection, providing token-efficient access to tools and resources.

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

Enables async-first working practices by providing tools to draft decision docs, convert meetings to async artifacts, score status updates, and triage sync vs async tasks. It also offers reference tools for the Open and Async book's principles and coaching prompts.

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.

Enables multi-party conditional escrow locking, autonomous proof-of-delivery release triggers, referral fee splits, and dispute freeze protection for agentic commerce and enterprise settlement workflows.

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 agents to verify claims with evidence-based truth scores and confidence levels by running a deterministic pipeline of evidence lanes and adversarial checks.

Enables MCP clients to interact with a safe, orchestrator-driven multi-agent builder that enforces governance constraints such as no deployment, no secret leakage, and grounded, non-hallucinated answers through an always-on governance gate.

Enables agents to deduplicate semantic prompt caches by measuring Jaccard token overlap, preventing redundant LLM calls and reducing cost. It provides deterministic lookup-or-insert cache behavior through an MCP-compatible interface.

Enables sentence-level factual grounding verification and hallucination detection for RAG outputs via MCP, with deterministic scoring and structured telemetry.

Enables sentence-level factual grounding verification and hallucination detection for RAG outputs via a native MCP server, providing deterministic scoring and structured telemetry.

Enables agents to run image A/B preference tests with human participants, retrieve live pricing and availability, create and purchase studies within configured USDC spending limits, and check vote results.

This MCP wraps Bitrefill public API to allow agents to search for products and shop using cryptocurrencies like Bitcoin, Ethereum, Solana, and many more.