"Scheduler that invokes the agent based on a predefined schedule" matching MCP servers:

A local-first knowledge base for LLM coding agents that indexes repository documentation, concept ontology, and build targets into Qdrant and exposes retrieval as MCP tools (search, get, list sources, reindex).

A self-evolving RAG system that enables AI agents to autonomously read and write memory, continuously learning and adapting user preferences, daily logs, and knowledge graphs across applications.

Gives MCP clients persistent personal memory and a versioned skill library, letting agents store facts, preferences, and decisions and recall them through hybrid semantic-plus-keyword search with automatic time weighting. Everything runs inside Cloudflare's free tier, with no server or database to operate.

A benchmarking MCP server that stores facts as both vector RAG and a knowledge graph, enabling head-to-head comparison of memory retrieval methods.

Enables hybrid search over policies using Reciprocal Rank Fusion and provides grounded, context-aware answers via a LangGraph agent with COSTAR prompting.

An enterprise-ready MCP server that exposes a RAG tool for retrieving relevant context and metadata from a Qdrant vector database using natural language queries.

Enables external clients to query a vector knowledge base about skills and roles through an MCP retrieval tool, supporting RAG-based question answering with tool-calling.

A local-first AI backend that combines Model Context Protocol, Retrieval-Augmented Generation, and an agent runtime to answer questions from a private knowledge base.

Enables cross-agent incremental memory retrieval and indexing for long coding sessions, preserving raw conversation logs across multiple CLI agents and recovering lossless context after compaction without local LLM generation.

Provides persistent memory for AI coding agents, enabling them to save, search (hybrid vector/BM25/concept graph), list sessions, and forget memories via MCP tools.

Retrieval-only MCP server that turns any knowledge source (Obsidian vault, notes, reference sets) into searchable Qdrant-backed skills, exposing list_skills, search_vault, and search_skill tools for agents to query via stdio or SSE.

Exposes search_knowledge and create_quote over Streamable HTTP with JWT validation, enabling permission-scoped knowledge retrieval and deterministic simulated quote creation while keeping pricing and approvals outside model control.

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

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

MCP server that provides secure read-only access to a local folder, enabling file listing, reading, semantic search (RAG), and indexing status via natural language, integrated with Claude Desktop and a custom agent loop.

Enables AI agents to search and analyze clinical trial data from ClinicalTrials.gov using both structured SQL queries for filtering trials by status, phase, and conditions, and semantic vector search for exploring detailed protocol information like exclusion criteria.

Enables AI agents to maintain long-term, cross-session memory by extracting facts, reconciling state conflicts, and retrieving relevant memories via vector search.

Enables MCP-capable coding agents to semantically search, store, and inspect a single local, cross-tool index of session histories and project docs. Agents can retrieve what other tools and prior sessions already discovered.

Integrates R2R (Retrieval-Augmented Generation) with Claude Desktop, enabling semantic search across knowledge bases and RAG-based question answering with support for vector, graph, web, and document search.

Provides persistent memory management for AI agents with 14 tools, including semantic search, context synthesis, auto-context middleware, and write-back actions to external services like Slack.