Enquire Mcp

Distribution status: this branch documents an unpublished source candidate. Candidate-specific npm commands and MCPB download URLs below are not currently usable. For available artifacts, use published GitHub releases or published npm versions. Stable-install commands remain separate from candidate instructions.

claude mcp add obsidian -- npx -y @oomkapwn/enquire-mcp serve --vault ~/Documents/Obsidian\ Vault

Your AI is brilliant. Its memory is fragmented.

Every new chat drops project history, decisions, research, and hard-won context. Vendor memory helps inside one product, then disappears when you move to another agent. Traditional file connectors can open a note when the path is already known; vector search can find a similar paragraph; neither is a complete memory system.

enquire-mcp turns Obsidian into the memory database for your agents

One install turns your existing vault into a persistent, queryable knowledge database for any MCP-compatible agent. It indexes locally, retrieves across formats, ranks by lexical + semantic + graph evidence, and returns the source context an agent can cite. The original files remain readable and editable without enquire-mcp.

Memory you own. Most conversation-memory products extract facts from chats into a separate store. enquire-mcp starts from the knowledge you deliberately kept: Markdown, frontmatter, wikilinks, PDFs, Canvas, and Bases. Recall is grounded in source material rather than a hidden paraphrase.

Document intelligence without a data migration. PDFs enter the same local hybrid-search path as Markdown and return page citations; OCR can recover scanned pages. Canvas becomes a typed graph. Dedicated tools run the supported Dataview LIST/TABLE subset and supported Obsidian Base filters instead of treating those formats as opaque attachments.

Freshness, not timeless recall. Every search hit can carry age_days and stale; optional recency weighting helps agents prefer newer knowledge and re-check old facts instead of confidently repeating them.

What makes enquire-mcp different:

1. Freshness-aware, cited recall. Search results retain source paths and expose age/staleness; PDF hits keep page citations. Optional recency re-ranking helps agents prefer fresh knowledge and re-verify old facts.
2. Read-only by default. Seven write tools stay invisible until --enable-write; privacy filters and exact-Origin HTTP admission reduce the exposed surface.
3. Obsidian-native document intelligence. Markdown/PDF hybrid recall is complemented by typed Canvas parsing, Dataview-style LIST/TABLE queries, and supported Base-filter execution.
4. Full-stack local retrieval. Hybrid BM25 + TF-IDF + multilingual embeddings fused via RRF, with an optional BGE cross-encoder reranker and per-signal scores; HNSW + int8 quantization scale the dense path.
5. A clear data boundary. enquire initiates zero outbound calls during serve and sends no telemetry. It returns requested context only to the MCP client you connect; that client's own privacy terms govern any later cloud processing. See the exact privacy policy.
6. Vendor-neutral memory. Your source knowledge remains in portable files. Switch agents or model providers without rebuilding a proprietary memory silo.

47 tools · 19 MCP prompts · 2367 declared test registrations · 50+ languages · v3.11.x stable + v4 SDK-v2 preview · semver-bound · MIT · npm build provenance (SLSA L2).

🏆 Why enquire-mcp is the complete Obsidian intelligence stack

Most alternatives solve one layer: an in-app similarity panel, a capable search engine, or MCP file access. enquire-mcp combines the full local retrieval ladder, agent orchestration, rich-document coverage, freshness, safety, and release discipline in one package.

_{✅ = the complete row is built in. ✕ = the complete combination was not documented on the reviewed public product surface; a project may implement part of the row or add it later. Review date: 2026-07-30. Exact source snapshots and row-by-row boundaries: competitive evidence.}

That is the TOP-1 thesis: one source of truth, one local intelligence layer, every agent — without reducing your knowledge to a cloud memory blob.

enquire-mcp is the open-source backend for Karpathy-style LLM Wikis on top of the Obsidian vault you already own: knowledge that compounds, with every answer traceable to source.

⚡ Quick start

npm install -g @oomkapwn/enquire-mcpenquire-mcp serve --vault ~/Documents/Obsidian\ Vault

Drop into any MCP client:

{  "mcpServers": {    "obsidian": {      "command": "npx",      "args": ["-y", "@oomkapwn/enquire-mcp", "serve", "--vault", "/path/to/vault"]    }  }}

Prefer a reviewable desktop bundle? Use MCPB Basic

The v4.0.0-rc.7 GitHub Release provides enquire-mcp-basic-4.0.0-rc.7.mcpb together with its checksum, inventory, SBOM, notices, and provenance. The bundle packages the server JavaScript and ordinary JavaScript dependencies; a compatible MCPB host must supply Node.js 22.13 or newer. Open it in that host, review the manifest, and choose the one vault directory the host may expose.

Basic means deliberately small: exactly 13 read-only tools, 0 prompts, and no write tools, watcher controls, persistent/on-disk index, embedding model, PDF, or OCR surface. Its recommended umbrella search lazily uses in-memory TF-IDF over live Markdown; the fixed launch contract also refuses discovery of a full edition's existing embedding database or watcher guard. enquire itself initiates no outbound calls while serving; requested note content still crosses into the MCP client you connected and is governed there by that client's privacy terms.

The release workflow requires the bundle's .sha256, build/release provenance, deterministic logical-content inventory, CycloneDX SBOM, and third-party license/notice inventory before publication. The archive byte stream is not claimed to be reproducible because the pinned upstream packer records pack-time metadata; verify the published checksum when exact bytes matter. Real desktop GUI installation, signing, directory-approval behavior, and any directory listing remain maintainer-gated acceptance work. Full hybrid retrieval remains available through the npm/CLI setup below.

📂 Config templates and agent lifecycle recipes in examples/ — Claude Desktop, Cursor, ChatGPT custom GPT (remote MCP over HTTP), plus client-neutral recall, evidence, freshness, synthesis, and safe-write playbooks.

Don't want to hand-assemble config? Let the CLI print the exact snippet for your vault + client (non-destructive — it writes nothing). Since v3.11.6:

enquire-mcp configure --vault <path>                 # prints config for every clientenquire-mcp configure --vault <path> --client cursor # just one (claude-code|cursor|vscode|codex|windsurf|claude-desktop|http)

The output is honest about each client's install boundary: VS Code gets its official review-and-install URI, Claude Code and Codex get copy-and-run commands, and clients whose one-click flow only accepts Marketplace/Registry entries are labeled copy-only with the exact fallback config. The generated vault path and physical package entrypoint remain visible for review before anything is saved.

Want full hybrid power? Complete the hybrid preflight, then serve:

npm install -g @oomkapwn/[email protected]      # exact prerelease packageenquire-mcp --version# recommended: preview first, then explicitly apply the same package-coherent planenquire-mcp first-run --tier hybrid --client claude-desktop --vault <path>enquire-mcp first-run --tier hybrid --client claude-desktop --vault <path> --apply# manual equivalent below: choose this instead of first-run --apply, not in additionenquire-mcp setup --vault <path>                          # caches embedder; builds FTS5 + embed-dbenquire-mcp install-model rerank-bge                      # caches the offline rerankerenquire-mcp doctor --tier hybrid --vault <path>           # structural/runtime readinessenquire-mcp configure --tier hybrid --client claude-desktop --vault <path>enquire-mcp serve --vault <path> --persistent-index --enable-reranker --use-hnsw

🤖 Set up in your AI agent — copy-paste prompts

Every connection now receives configuration-aware initialize.instructions: the recommended recall workflow, citation and freshness semantics, active write posture, and the rule that retrieved vault content is data rather than instructions. Clients that surface MCP server instructions can use that contract automatically. The copy-paste prompts below remain useful as an explicit user preference or for hosts that do not expose server instructions.

# Add the MCP server to your Claude Code config (one time)claude mcp add obsidian -- npx -y @oomkapwn/enquire-mcp serve --vault ~/Documents/Obsidian\ Vault

enquire-mcp configure --client vscode --vault <path>

enquire-mcp configure --client codex --vault <path>

Reusable agent rule (drop into any AGENTS.md / CLAUDE.md / .cursorrules so the agent knows when to reach for the vault):

When my question touches my own notes, decisions, projects, people, or research, search my Obsidian vault first via the obsidian_* tools (start with obsidian_search) and cite the source note on every fact. Prefer enquire for conceptual / cross-language / "what did I say about X" recall; use plain grep / ripgrep for exact literal strings. If nothing relevant comes back, say so — don't guess.

Example queries that work well

• "Find every note where I discussed pricing strategy, summarize the evolution." — RRF fusion + reranker handles "evolution" semantically
• "What was my decision on PostgreSQL vs MongoDB? Cite the daily note." — when fused candidates have equal RRF scores, candidate-set wikilink in-degree breaks the tie
• "Анализируй мои заметки о RAG за последние 3 месяца" — multilingual embeddings + frontmatter date filter
• "What pages of the LLaMA-3 paper PDF talk about scaling?" — PDFs blended into search with [page: N] citations
• "Show me topical communities in my research vault — what themes have I been exploring?" — obsidian_get_communities (GraphRAG-light)

🧠 Use cases

1 — Long-term memory for AI agents. Drop your Obsidian vault into any MCP-compatible agent (Claude Code, Claude Desktop, Cursor, ChatGPT, Codex, OpenClaw). The agent now has durable, semantic recall over every meeting note, journal entry, research log, and decision doc you've ever written — across sessions, models, and providers. Your knowledge isn't locked into one vendor's memory layer; it lives in plain markdown you own and can migrate freely.

2 — Personal knowledge base / second brain. Hybrid retrieval surfaces the right note for any phrasing, in any of 50+ languages. Ask in English about a Russian-language journal entry from 2 years ago, get the right hit. Wikilink graph-boost only breaks equal RRF-score ties using in-degree within the candidate set. GraphRAG-light surfaces topical communities — discover connections you forgot you made. PDFs blend into search with [page: N] citations so research papers and meeting transcripts become first-class memory.

3 — Agentic RAG / context engineering. obsidian_search exposes per-signal scores so the agent sees why each hit ranked. HyDE pre-rewrites vague queries into rich hypothetical answers before retrieval. Sub-question decomposition handles multi-hop questions ("how did our pricing strategy evolve and what was the customer reaction?") by breaking them into independent sub-queries, fusing results. The built-in eval harness (NDCG / Recall / MRR) lets you measure retrieval quality on your own queries instead of trusting vendor benchmarks.

✅ Built for serious local knowledge workflows

Choose enquire-mcp when you want:

• Your Obsidian vault to remain the source of truth instead of copying knowledge into another proprietary store.
• One memory layer across many AI agents so switching models never means starting over.
• Conceptual and multilingual recall that survives different wording, not only exact string matches.
• Cited, inspectable answers with note paths, PDF pages, signal scores, and freshness metadata.
• Local-first privacy with read-only defaults, explicit write gates, zero outbound calls initiated by enquire during serve, and a documented MCP-client trust boundary.
• A complete retrieval backend spanning hybrid search, reranking, graph context, agentic expansion, rich Obsidian formats, and remote MCP.

Clear scope: enquire-mcp is a headless MCP server / CLI for Markdown, Canvas, Bases, and PDF knowledge. Use exact-search tools alongside it for literal tokens; use the built-in HTTP transport when agents need remote access.

📖 Product site & API reference

The product front door explains the cited-memory outcome, proof, client paths, and AI-readable resources. The complete auto-generated API reference documents every tool, prompt, and exported helper with full TSDoc (@param / @returns / @example). Both are rebuilt from source on every push to main via publish-docs.yml; historical deep TypeDoc URLs remain valid.

🏗️ How retrieval works

graph LR    Q[Query] --> S[obsidian_search]    S --> BM25[BM25 / FTS5]    S --> TFIDF[TF-IDF cosine]    S --> EMB[ML embeddings<br/>HNSW]    BM25 --> RRF{RRF fusion<br/>k=60}    TFIDF --> RRF    EMB --> RRF    RRF --> GB[Graph boost<br/>in-degree tie-break]    GB --> RR[BGE cross-encoder<br/>reranker]    RR --> R[Ranked hits<br/>per_signal observability]

obsidian_search auto-detects available signals and gracefully degrades. Wikilink graph-boost uses candidate-set in-degree only to break equal RRF-score ties. Optional cross-encoder reranking re-scores top-N for +15.5 NDCG@10 measured. Every hit returns per_signal: { bm25, tfidf, embeddings } so you see WHY it ranked.

🛠️ All 47 tools

47 production tools total: 35 always-on read tools (incl. the umbrella obsidian_search) + 4 opt-in read + 7 gated writes + 1 closed-loop feedback. Full reference: docs/api.md.

Plus 3 MCP resources (obsidian://vault/info, obsidian://note/{path}, obsidian://chunk/{n}/{path}) and 19 MCP prompts (summarize_recent_edits · review_tag · find_orphans · weekly_review · extract_todos · process_inbox · consolidate_tags · find_duplicates · lint_wiki · monthly_review · search_with_query_expansion · vault_synth · vault_wiki_compile · vault_lint_extended · vault_capture · vault_persona_search · vault_automation_setup · vault_research · vault_synthesis_page) for common vault workflows.

🛡️ Trust

Full posture and privacy policy: SECURITY.md · Stability surface: STABILITY.md · Vulns: [email protected].

❓ FAQ

Need Obsidian installed? No. Reads .md + .canvas + .pdf directly. Works against any Obsidian-format vault.

Will it write to my vault? Not unless you pass --enable-write. All 7 write tools are gated; destructive ones support dry_run.

Data sent anywhere? enquire sends no telemetry and initiates no outbound HTTP during serve. It does return requested vault context to the MCP client you connect; a cloud client may process that context under its own privacy policy, and any tunnel/proxy is another trust boundary. Explicit acquisition commands—setup, build-embeddings, and install-model—may fetch ONNX weights from Hugging Face; a hybrid-tier first-run --apply orchestrates those same acquisitions, while install-ocr-lang fetches a Tesseract language pack. Exact policy: SECURITY.md.

Performance? It depends on vault size, hardware, model, and enabled retrieval layers. The public evidence includes a production report of 50–100ms BM25 top-10 at 1,771 chunks / 368 files plus a reproducible synthetic benchmark showing 37–103× FTS5 speedup over linear scan at 100–1,000 notes. Run the built-in eval and benchmark commands on your vault before setting a latency SLO; see benchmarks and the FTS5 implementation note.

Languages? The default embedder is paraphrase-multilingual-MiniLM-L12-v2 (50+ languages), validated end-to-end on Russian + English bilingual vaults. The default cross-encoder reranker is rerank-bge (English-only; the only catalog alias verified end-to-end); multilingual reranker aliases currently fail their transformers.js tokenizer compatibility check. CJK/Thai/Khmer tokenization uses Intl.Segmenter.

Run remotely? Yes — serve-http exposes the same server over Streamable HTTP. In the v4 preview, strict modern 2026-07-28 traffic and supported legacy clients use separate official SDK v2 paths backed by one registered surface; malformed or unsupported modern claims never fall back to legacy. Front with Tailscale Funnel or Cloudflare Tunnel for HTTPS. Works with claude.ai web, ChatGPT custom GPT, Cursor HTTP mode, and mobile MCP clients. See docs/http-transport.md.

🚀 Releases

v3.0.0 — stable channel. The v2.x retrieval roadmap is complete and the public surface is now semver-bound. Highlight reel:

v2.0 hybrid retrieval (BM25+TF-IDF+embeddings via RRF) · v2.6 remote MCP · v2.7-2.8 PDFs blended · v2.9 BGE reranker · v2.10 OCR · v2.11 doctor + setup · v2.12 eval harness · v2.13 HNSW · v2.14 stateful sessions · v2.15 late-chunking · v2.16 HNSW persistence · v2.17 int8 quantization · v3.8.0 stable · v3.8.7 HTTP transport hardening · v3.9.0 stable: OCR'd PDF watcher embed-sync, HNSW in-memory live update on file changes, R-10 adaptive HNSW refill (closes the >66% excluded under-return). · v3.10 stable: forgetting-aware freshness — age_days + stale flag + opt-in --recency-weight re-ranking + frontmatter-aware obsidian_search.

Channel: npm install @oomkapwn/enquire-mcp → latest stable (@latest = v3.11.x). Pre-release: npm install @oomkapwn/enquire-mcp@rc → the v4 SDK-v2 candidate; pin @4.0.0-rc.7 for an exact preview install. Full changelog: CHANGELOG.md · Forward plan: ROADMAP.md.

🤝 Contributing

git clone https://github.com/oomkapwn/enquire-mcp.gitcd enquire-mcp && npm installnpm test       # full suite (2367 declared test registrations)npm run lint   # zero warningsnpm run build  # tsc → dist/

Issues, PRs, ideas welcome. For setup questions, bug reports, and private security routing, see SUPPORT.md.

📜 License

MIT. Built by Alex (@OomkaBear). Named after Tim Berners-Lee's 1980 prototype of the WWW — the original hypertext system, before the web. The original spec was: you could ask the system anything. enquire-mcp brings that to your vault.