Mnē-MCP — MCP RAG Server for opencode

Mnē-MCP (from the Greek Mnēmosynē, Titaness of memory; the macron in ē, U+0113, marks the long vowel in the transliteration) is an MCP server that implements a complete RAG (Retrieval-Augmented Generation) pipeline for opencode: it indexes an Obsidian vault or document folders and answers assistant questions using your own notes as a source, citing the exact path of each fragment used.

Stack: #RAG #MCP #mcp-server #opencode #Obsidian #Qdrant #Ollama #DeepSeek #bge-m3 #Python

Features

1. Incremental indexing — scans directories (.md, .txt) from disk with change detection via mtime + md5, stale cleanup, and support for large volumes (~19 GB).

2. Local embeddings — bge-m3 model via local Ollama (/api/embed), 1024 dimensions, multilingual (including Spanish), L2-normalized to scale correctly with cosine similarity.

3. Semantic retrieval — Qdrant (Docker), single collection with payload filters (source, path, mtime, md5, heading_path) and configurable relevance threshold.

4. Grounded generation — answers with deepseek-v4-flash (Ollama Cloud) only when evidence exceeds the threshold; otherwise returns explicit rejection ("no relevant documents found"). Without evidence, it does not generate.

Related MCP server: mcp-duckvault

Architecture

MCP tools (stdio) ──┐
                    ├── RagService (core) ────▶ Qdrant (vectorial, Docker)
Admin page (:8310) ─┘       │
                            ├── OllamaEmbedder (bge-m3 local)
                            └── LLMProvider (deepseek-v4-flash, Ollama Cloud)

• Python 3.14 · official MCP SDK (mcp==1.12.4) · qdrant-client · httpx

• 7 tools: index · query · search · delete · list · stats · config

• Local admin page (http://127.0.0.1:8310): dashboard, indexed documents, asynchronous indexing with progress, and search playground. Shares the same core as the MCP tools (zero duplicated logic).

• Suite of 70 offline tests (Qdrant :memory: + mocks) + end-to-end smoke test.

Developer Usage

docker compose up -d qdrant   # Qdrant
ollama pull bge-m3            # embeddings locales
cp .env.example .env          # OLLAMA_API_KEY + VAULT_ROOT
make install                  # instala el paquete (venv)
make admin                    # panel: http://127.0.0.1:8310

Register in opencode.json (mcp section, local type, command python3 mcp_rag/server.py or the mcp-rag-opencode entry point).

Roadmap

Hybrid dense+sparse search (BM25 + RRF) · cross-encoder reranking · semantic cache · evaluation harness (recall@k, MRR, nDCG) — planned in openspec/specs/mnemos-differentiators/.

Academic

Integrated Project · Prof. Christian Pérez · Student: Gat · 2026