Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
Descripción del creador (original en inglés)
Local-first memory & context-handoff across AI tools — capture in one, resume in another. No cloud.
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.
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": {
"mindmap": {
"command": "npx",
"args": [
"-y",
"@ravi-labs/mindmap-mcp-server"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add mindmap -- npx -y @ravi-labs/mindmap-mcp-serverPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"mindmap": {
"command": "npx",
"args": [
"-y",
"@ravi-labs/mindmap-mcp-server"
]
}
}
}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": {
"mindmap": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@ravi-labs/mindmap-mcp-server"
]
}
}
}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.
Esta 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 ravi-labs, 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.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
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 (0.5.0) llegó al registro hace 4 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
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
Agent-Native Google Calendar - manage events, sync, and public booking
Create and edit Excel files with AI: charts, PivotTables, formulas. No Excel needed.
Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.
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.
Ever lose the thread of a discussion across Claude Code, Claude Desktop, and your other AI tools? Mind Map is a small, local-first MCP server that acts as a shared memory layer across all your local MCP clients.
You capture context at the end of a session, and resume it in any other tool — no more re-explaining your project from scratch. Memory you stop using cools and decays gracefully into one-line traces (it's never silently deleted), so your shelf stays small and trustworthy instead of becoming another haystack.
Local-first by design. It runs on your machine; your memories are plain
files in
~/.mindmapthat never leave your control. No account, no cloud, no
server to trust. Install once with
npx @ravi-labs/mindmap-mcp-server install.
_ChatGPT and other cloud/web clients are out of scope for now — they can't
reach a local server without a public endpoint, which conflicts with
local-first. See Future._
Once published to npm, the whole install is:
npx @ravi-labs/mindmap-mcp-server install
This auto-detects your local MCP clients (Claude Desktop, Cursor, Windsurf) and writes the config for you, and configures Claude Code via its CLI if present. Restart your client and Mind Map's tools are there. Preview first with npx @ravi-labs/mindmap-mcp-server install --dry-run; undo with npx @ravi-labs/mindmap-mcp-server uninstall.
Data lives in ~/.mindmap/ by default — override with the MINDMAP_DIR env var.
Every client uses the same command — npx -y @ravi-labs/mindmap-mcp-server over stdio.
Claude Code:
claude mcp add mindmap -- npx -y @ravi-labs/mindmap-mcp-server
Claude Desktop — edit claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": { "mindmap": { "command": "npx", "args": ["-y", "@ravi-labs/mindmap-mcp-server"] } } }
Cursor / Windsurf / other clients — same command + args shape in that client's MCP config. Add an env block to relocate data:
{ "command": "npx", "args": ["-y", "@ravi-labs/mindmap-mcp-server"],
"env": { "MINDMAP_DIR": "/path/to/my/memory" } }
git clone https://github.com/ravi-labs/mindmap-mcp-server.git && cd mindmap-mcp-server
npm install && npm run build node dist/index.js install --local # points clients at this checkout
Just talk naturally inside your AI tool — the model calls the right tool:
Run npx @ravi-labs/mindmap-mcp-server quickstart for the full getting-started guide.
Start an idea in Claude, keep going in Cursor — without re-explaining it. Mind Map is the shared memory; each tool brings its own brainstorming muscle. mindmap_brainstorm loads your persona and prior idea-threads on the topic, you brainstorm, and saving with kind: "brainstorm" lets it resume anywhere. A bundled brainstorm Skill (installed into ~/.claude/skills by install) wires the load → brainstorm → save flow for Claude.
Mind Map always organizes automatically (recency tiers, source, topic, workspace). Collections are your own layer on top: named groups like 🎯 Goals, 🅿️ Parking Lot, ✅ Decisions — or anything you invent. Say *"add this to my Goals"* or *"park this"* in any tool, or use the dashboard's Organize tab.
Two things make collections more than folders:
mindmap_resume drops you back in: full topic context, right folder, exact next step.Don't want to file things by hand? Auto-organize reads across *all* your sessions and proposes collections — it always looks for unfinished projects (started building, went cold, never shipped), Goals, Decisions, and parked ideas, plus anything you ask for (*"everything about billing"*). It only suggests; nothing is filed until you accept. Uses your own LLM if configured, otherwise a no-key heuristic.
For Claude Code, install adds a `SessionEnd` auto-capture hook — every substantive session is distilled into a memory automatically, so you never have to remember to save. It's deduped (resuming + re-ending updates in place) and skips trivial/automated sessions. Opt out with install --no-hook; it's removed cleanly by uninstall.
For other clients (or as a belt-and-braces nudge), add one line to your client's instructions (e.g. Claude Code's CLAUDE.md):
At the end of a substantive session, call
mindmap_captureto save the context.
When I reference past work, call
mindmap_resumefirst.
The tools are also described to encourage the model to do this proactively.
Import your existing Claude history into Mind Map — distilled, not raw dumps:
npx @ravi-labs/mindmap-mcp-server import --dry-run # preview
npx @ravi-labs/mindmap-mcp-server import # apply
Covers Claude Code (CLI — full prompts), Cowork (title + opening message), VS Code Copilot (full prompts), and Cursor (chat titles + prompts); each memory is tagged by source. Filters: --source code|cowork|copilot|cursor, --project , --limit N. Imported memories keep their original dates, so old ones settle into cold traces automatically.
Imported memories capture the discussion (your prompts + the assistant's substantive answers), not just titles — for the transcript-backed sources (Claude Code, Cursor, Copilot). Re-run with --reimport to refresh existing memories in place after an upgrade.
Notes:
npx @ravi-labs/mindmap-mcp-server cleanup --dry-run # preview
npx @ravi-labs/mindmap-mcp-server cleanup # apply
Removes automated/scheduled-task memories and collapses duplicate sessions. Anything you've promoted is always kept.
Beyond individual discussions, Mind Map keeps a persona: a distilled, evolving profile of *how you work* — your stack, style, communication preferences, and constraints — so any tool can apply it and stop re-asking the same setup questions. It's separate from your discussion memories.
mindmap_persona_set. High confidence, yours, editable.mindmap_persona_learn derives recurring signals from your existing memories. No LLM needed (keyword heuristic); richer if you enable one.mindmap_persona at the start of a session and apply what's there.Add this to your client's instructions so it happens automatically:
At the start of a session call
mindmap_personaand apply it. When I state a
durable preference, call
mindmap_persona_set.
A memory you can't extract, inspect, or carry isn't a memory you own. Three features make Mind Map's memory genuinely yours:
Memory Passport — export everything to one portable, open file, and pull your context *out of the walled gardens*:
npx @ravi-labs/mindmap-mcp-server passport export # → ~/mindmap-passport-<date>.json
npx @ravi-labs/mindmap-mcp-server passport import # from another machine npx @ravi-labs/mindmap-mcp-server passport import-chatgpt conversations.json # your ChatGPT export npx @ravi-labs/mindmap-mcp-server passport import-claude conversations.json # your Claude.ai export
The cloud chats can't be reached live from a local server — but their data export files are yours, and this imports them as distilled memories.
Glass-box memory — see exactly what's stored and why:
npx @ravi-labs/mindmap-mcp-server audit
Every memory shows its provenance (where it came from), trust (how many times you reused it), and decay forecast (when it fades to a one-line trace). In the dashboard, each memory has a one-click Forget. Unlike opaque vector stores, nothing about your memory is hidden from you.
Persona projection — write your persona into each tool's own config, so even non-MCP tools know how you work:
npx @ravi-labs/mindmap-mcp-server persona-sync # Claude, Cursor, Copilot, Windsurf
It edits only a managed block ( …:end), so your own content is never touched.
Mind Map runs fully without any LLM — every feature has a no-LLM path. If you *want* smarter persona inference and topic-graph labels, plug in your own provider (anthropic, openai, google, or local ollama). It's opt-in and graceful: no key, or a failed call, simply falls back to the no-LLM path, and any cost notes are rough estimates — never a bill.
Mind Map never stores your API key. It saves only the provider + model name in ~/.mindmap/llm.json; the key is read from an environment variable at call time. You set the key; Mind Map just reads it.
Smarter search (optional). Search is BM25 out of the box — no LLM needed. With an embeddings-capable provider (OpenAI, Google, or Ollama — Anthropic has no embeddings API), build a local embedding cache for hybrid search (BM25 fused with semantic similarity via reciprocal rank fusion):
npx @ravi-labs/miSolo se muestra el principio; lee el resto en el repo