"Registering the revanthlevaka/OpenYantra repository" matching MCP servers:

100% local-first persistent memory engine and Desktop HUD (Cmd+Shift+Y) for Cursor and Claude Desktop with SQLite, open .ods files, and bi-directional Obsidian sync.

MCP server that provides a citation-first memory layer for AI agents, enabling verified search across Git-backed repositories and optional conversation memory. It exposes tools for doctor, sync, search, get, init, and ingest, returning JSON results with verified citations or abstain status.

Enables AI agents to search, read, and traverse a local knowledge base of Markdown files using full-text search and relationship graph, reducing token usage.

An MCP server that manages your conversation context automatically, pruning dead branches and keeping an audit trail of every cut.

Enables AI agents to search and modify a personal knowledge base through OAuth-secured MCP tools, covering Obsidian vaults and Google Drive mirrors with semantic search, note read/write operations, audit logging, and eval-gated local classification.

Provides AI assistants with persistent graph database memory using Neo4j, enabling task management, relationship understanding, semantic search with embeddings, file indexing, and multi-agent coordination through the Model Context Protocol.

Model-agnostic, agent-ready Obsidian MCP server with RBAC, SLSA provenance, and native search. 163 tools across 31 domains, multi-vault, pluggable embeddings.

A local-first personal archive with a read-only MCP gateway that preserves files and AI conversations, derives evidence-linked memory candidates using local models, and enables bounded context retrieval for questions.

Hosted MCP gateway to the RTP global commons of neighborhood practice and relational tech knowledge. AI builders can query 275+ recipes, frameworks, and methodology docs for community organizing.

An MCP server that provides AI tools with relational tech principles, patterns, and the Studio library to guide builders in creating community-centered tools using embedded design.

Local-first MCP server that enables multiple Claude agents to coordinate through a shared message bus with SQLite persistence and real-time clock anchoring.

Enables hair colourists to capture a client's colour formula by voice, retrieve it on the next visit, and scale the mix to today's bowl, with per-client memory and clarification when names are ambiguous.

Enables an AI agent to search a folder of markdown notes, follow links and backlinks, and safely rename notes while rewriting every link to them.

An MCP server that enables LLMs to pull-based search through Clawket's RAG repository for exploratory and conditional queries. It provides read-only access to search artifacts, tasks, and decisions via HTTP API.

Enables Claude Code to access a team's handbook (markdown repository) through listing, reading, and searching documents, helping standardize processes and troubleshooting.

Local-first shared memory and coordination layer for AI coding agents, with repository evidence, reservations, handoffs, code graph context, and dashboard review backed by PostgreSQL/pgvector.

Gives every AI agent working in a repository a shared, persistent knowledge graph stored in a plain JSONL file, with each entity, relation, and observation tagged with the date and agent that added it. Provides tools and prompts for searching, editing, renaming, merging, cleaning up, and exporting that memory, and keeps it locked to the repository that owns it.

Enables coding agents to search and retrieve persistent repository memory created by Cliper, providing context, gaps, architecture, and timeline information via MCP tools.

Exposes Microsoft BCQuality repository (Business Central development best practices) as structured tools for AI agents, enabling rule discovery, search, and application via MCP protocol.

Enables teams to share structured, git-synced context among AI coding agents working on the same repository, including living plans, task declarations, handoff briefs, file-provenance history, and conflict detection.

Enables code agents to persist and recall durable project knowledge — rules, decisions, procedures, learnings and secrets — across sessions at low context cost, with accent-insensitive PT-BR search, scoped per repository/workspace, and secrets that stay out of the model's context.

Repository-native protocol and MCP server for coordinating work items, documentation, changelogs, and project memory between humans and AI agents, using Markdown files in a Git repository as the canonical data source.

Serves organizational context to MCP-capable AI agents from a local folder or GitHub repository, and lets agents propose changes that require human approval.

This MCP server lets any compatible AI assistant read, extend and connect the matters in a person's life — plans, trips, people and places — from a repository that belongs to the user, so a new assistant picks up where another left off. It keeps the user's words verbatim, records every change, and stores the repository as a plain file on the user's own computer.

Turns any GitHub repository into your organisation's persistent context layer, enabling AI agents to read, search, and propose updates via pull requests.

Provides persistent, local, repository-aware memory for MCP-compatible coding agents, enabling startup context, storing corrections and decisions, searching memories, and archiving obsolete guidance.

A local MCP server that keeps one source of truth about capabilities, accomplishments, and positioning consistent with derived profiles, applications, and résumés, by registering decisions, checking consistency, and assembling materials for media-specific documents.

Turns a repository into a deterministic, local-first knowledge graph for coding agents, enabling navigation of code structure and relationships via MCP tools.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Enables AI coding assistants to recall and manage repository-aware memory, including project ingestion, status monitoring, memory retrieval, and explicit content ingestion via the CueMap engine.