Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Self-hosted agent memory as a markdown vault: semantic and keyword search, wikilink graph, OAuth
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
DATABASE_URL, VAULT_PATH, SECRET_KEY, MCP_HOSTNAME, EMBEDDING_PROVIDER, OLLAMA_URL, OPENAI_API_KEYLos valores marcados como secretos son llaves de tu cuenta. Nunca los compartas ni los pegues en un chat.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"obsidian": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"DATABASE_URL",
"-e",
"VAULT_PATH",
"-e",
"SECRET_KEY",
"-e",
"MCP_HOSTNAME",
"-e",
"EMBEDDING_PROVIDER",
"-e",
"OLLAMA_URL",
"-e",
"OPENAI_API_KEY",
"ghcr.io/maxkuminov/obsidian-mcp:v1.1.0"
],
"env": {
"DATABASE_URL": "your-value",
"VAULT_PATH": "your-value",
"SECRET_KEY": "your-value",
"MCP_HOSTNAME": "your-value",
"EMBEDDING_PROVIDER": "your-value",
"OLLAMA_URL": "your-value",
"OPENAI_API_KEY": "your-value"
}
}
}
}Ejecuta esto en tu terminal
claude mcp add obsidian --env DATABASE_URL=your-value --env VAULT_PATH=your-value --env SECRET_KEY=your-value --env MCP_HOSTNAME=your-value --env EMBEDDING_PROVIDER=your-value --env OLLAMA_URL=your-value --env OPENAI_API_KEY=your-value -- docker run -i --rm -e DATABASE_URL -e VAULT_PATH -e SECRET_KEY -e MCP_HOSTNAME -e EMBEDDING_PROVIDER -e OLLAMA_URL -e OPENAI_API_KEY ghcr.io/maxkuminov/obsidian-mcp:v1.1.0Completa los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"obsidian": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"DATABASE_URL",
"-e",
"VAULT_PATH",
"-e",
"SECRET_KEY",
"-e",
"MCP_HOSTNAME",
"-e",
"EMBEDDING_PROVIDER",
"-e",
"OLLAMA_URL",
"-e",
"OPENAI_API_KEY",
"ghcr.io/maxkuminov/obsidian-mcp:v1.1.0"
],
"env": {
"DATABASE_URL": "your-value",
"VAULT_PATH": "your-value",
"SECRET_KEY": "your-value",
"MCP_HOSTNAME": "your-value",
"EMBEDDING_PROVIDER": "your-value",
"OLLAMA_URL": "your-value",
"OPENAI_API_KEY": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"obsidian": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"DATABASE_URL",
"-e",
"VAULT_PATH",
"-e",
"SECRET_KEY",
"-e",
"MCP_HOSTNAME",
"-e",
"EMBEDDING_PROVIDER",
"-e",
"OLLAMA_URL",
"-e",
"OPENAI_API_KEY",
"ghcr.io/maxkuminov/obsidian-mcp:v1.1.0"
],
"env": {
"DATABASE_URL": "your-value",
"VAULT_PATH": "your-value",
"SECRET_KEY": "your-value",
"MCP_HOSTNAME": "your-value",
"EMBEDDING_PROVIDER": "your-value",
"OLLAMA_URL": "your-value",
"OPENAI_API_KEY": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
EMBEDDING_PROVIDER envOLLAMA_URL envEsta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub maxkuminov, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
Debes completar: DATABASE_URL, VAULT_PATH, SECRET_KEY, MCP_HOSTNAME, OPENAI_API_KEY
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (1.1.0) llegó al registro anteayer.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Docker en tu computadora.
En la misma categoría: Context7, Codebase Memory, Exa
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
Your agents' memory, in the vault you already have, reachable from claude.ai, ChatGPT, Claude Code and your phone.
A self-hosted Model Context Protocol server over an Obsidian vault. Three things it does that the alternatives mostly don't:
expected_hash preconditions refuse an edit made from a stale read, every publish is atomic and no-clobber by default, deletes go to .trash/, and dry_run returns the diff before anything lands.Connect from Claude Code:
claude mcp add obsidian --transport http \
--url "https://obsidian-mcp. /mcp" \ --header "Authorization: Bearer omcp_..."
In claude.ai, Claude Desktop or ChatGPT, add a custom connector at https://obsidian-mcp. /mcp and sign in through the OAuth consent page. Connect a client has the rest.
Stack: Python 3.12, FastAPI, PostgreSQL with pgvector. Pluggable embeddings (Ollama — bge-m3 by default, qwen3-embedding:0.6b in the reference deployment — or OpenAI text-embedding-3-{small,large}).
There are three things going on here, and they're more interesting together than apart.
If you let an agent run for a while, it needs memory. Most setups solve this with an opaque vector store, a SQLite blob, or a managed "memory" service that you can't see into. That works until you want to know what the agent thinks it knows about you, or you need to correct something, or you want to understand why it just made a weird suggestion.
This server gives you a different deal. Agent memory lives as markdown files in your vault. Folder structure, file names, frontmatter, all visible. You can open the file in Obsidian and read it. You can edit it. You can delete it. You can grep it. The agent's "memory" is a human-auditable artifact that sits in the same place as your own notes, with the same tools available.
The home lab is the use case that sold me on this. My vault has notes on the rack, the network, and every Home Assistant integration. I can say "set up a night-light mode in the master bathroom, 1% after 11pm" and a sysadmin agent finds the right config, makes the change, and updates the doc in the same pass. Six months later when I've forgotten how it works, the answer is in the vault, not buried in some chat history I can't search.
The semantic search and wikilink graph still work over that material, so retrieval is fast and conceptual. But the substrate is files you own, not a black box.
To be precise about the scope: what the server supplies is MCP-accessible storage, keyword and semantic search, and graph operations over markdown notes, for whatever MCP clients you connect. The agents direct their own reads and writes. There is no extraction or decay pipeline running behind them, and nothing rewrites a note because a model judged it stale: an agent remembers something because it wrote a note, and forgets it because someone deleted one. By late 2026 that is less of a contrarian position than it sounds. Every vendor's consolidation step (the "dreaming" or "sleep-time" job) has turned into a separate process you can inspect, rather than silent extraction inside the write path. This server keeps the agent as the author. The consolidation help it will grow is a hygiene report that proposes merges and flags duplicates, and never writes (#359), not a pipeline that edits your notes while you are not looking.
The other half runs the other way: the vault isn't only the agents' memory, it's mine. I think of my Obsidian vault as my exocortex. The "big me" that includes notes, calendars, scripts, search, and AI assistants is substantially more capable than the "small me" of the biological brain alone. It's also where I do most of my thinking, because writing something down is itself a form of thought.
The problem is that until recently, the vault was passive. I had to go find things. Agents that wanted to help me had to be briefed from scratch every session, and they had no way to see what I'd already written about a topic.
This server fixes that. Now the same vault feeds my own daily writing and any agent I plug into it. The agent reads what I read, links what I link, follows the same wikilinks, sees the same frontmatter. When I write a project note on Sunday, my Monday-morning briefing agent already knows about it. When the agent leaves notes from a research session, they show up in my normal Obsidian search.
A concrete version of this: I'll spend a session in Claude Code on a project, wrap up, push the commits, and then just say "update Obsidian." The agent reads the vault guide, figures out where project notes live in my structure, picks the right format and frontmatter, and leaves a session log I can later roll into a status report. No path-passing, no telling it what to write — the conventions are already in the vault, and it follows them.
That's the exocortex idea made concrete: one place that holds context, and both the human and the agents reading and writing into it on the same terms.
The thing that still surprises me is that this is internet-facing. Same vault, same notes, reachable from anywhere I happen to be talking to an agent — Claude in a browser tab, Claude on my phone on the train, an n8n workflow firing on a schedule, a Claude Code session on whatever laptop is in front of me. All behind OAuth or a bearer key, all hitting the same source of truth.
So when I hear an interesting podcast on a commute and want to think out loud about it with Claude on my phone, the agent isn't starting cold. It can pull up what I've already written on adjacent topics, surface a note I half-finished six months ago, and at the end of the conversation suggest updates and write them in. The vault doesn't have to be near me to be the thing I'm working in. (A session away from the keyboard walks through exactly one of these.)
To make the abstract concrete, a short transcript from a real session. Tool calls inline; the user types in plain English and the agent picks the right tool.
you > find anything I've written about why retrieval-augmented
chat is hard
claude → semantic_search(query="why retrieval-augmented chat is hard") • 2025-12-03 - RAG failure modes.md • 2025-10-18 - Chunking tradeoffs.md • 2024-06-22 - Vector recall vs precision.md
the closest hit is your December note on RAG failure modes. let me see what it's connected to.
→ get_neighborhood(path="2025-12-03 - RAG failure modes.md", depth=1) 8 connected notes. notable ones: • "Hybrid search" — a re-ranking design you sketched but didn't ship • "Out-of-distribution queries" — a half-finished draft from October • "Embedding model choice" — links to bge-m3, your current model
you have three connected pieces here: a failure-modes catalog, an unshipped hybrid-search sketch, and an abandoned O-O-D draft. want me to summarize all three together, or pull up the draft so you can decide whether to finish it?
you > pull up the draft, and tag it #revisit-2026 in frontmatter while you're there
claude → read_note(path="2025-10-18 - OOD queries (draft).md") → set_frontmatter( path="2025-10-18 - OOD queries (draft).md", updates={"tags": ["draft", "rag", "revisit-2026"]} )
here's the draft, and the tag is on. you stopped right before the section on confidence thresholds; the open question you left yourself was…
Two things to notice. First, the agent didn't need to be told what folder to look in or what tools to use — it picked them. Second, the write at the end is structured (set_frontmatter mutating YAML, not a regex over the file body), so the note round-trips cleanly. The self-describing vault and the wikilink graph are doing the work that makes this feel natural.
The transcript above is the easy case: I'm at a desk, I can see what the agent is doing, and Obsidian is one alt-tab away. The session that actually changed how I think about this server had none of that.
I was out walking with a health podcast in my ears — a long one, two people who clearly disagreed with each other, an hour of it. I had my phone and no intention of going home to a laptop. So I pulled the episode's transcript, handed it to Claude on my phone, and we talked it through while I kept walking: what the actual claim was, which parts I already had notes on, where it cut against something I'd decided months ago and written down at the time.
The agent had the vault the whole way. It surfaced what I'd already written on the topic, flagged that two dates in an older note were wrong, and asked whether a decision I'd recorded last year still stood given what the episode argued. By the time I got back it had written all of it in: the health-related decisions I'd actually landed on during the walk, the date corrections in the old note, a couple of new notes on the episode itself — and, because the conversation kept circling back to it, a durable note on how I decide which experts to trust on medical questions in the first place. That last one is the artifact I keep returning to. It wasn't about the episode at all; it was the reasoning underneath a whole class of decisions, and it now sits in the vault where the next agent will find it.
I never opened Obsidian. Not on the walk, not when I got home. The whole session — retrieval, argument, correction, and the writing that came out of it — went through an agent, and the vault is simply where it landed. Obsidian is how I check the w
Solo se muestra el principio; lee el resto en el repo