Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Search, read and build your Filamental knowledge graph. Local-first: notes stay on your computer.
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": {
"filamental": {
"command": "npx",
"args": [
"-y",
"filamental-mcp"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add filamental -- npx -y filamental-mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"filamental": {
"command": "npx",
"args": [
"-y",
"filamental-mcp"
]
}
}
}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": {
"filamental": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"filamental-mcp"
]
}
}
}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 Scottnine, 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 (0.2.17) llegó al registro hace 8 días.
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: 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.
The Model Context Protocol server for Filamental. It lets Claude and other AI apps search, read and build the knowledge graph in your Filamental project: the same markdown files the app shows you, so every change appears in the app as it happens.
The server runs on your computer, works whether Filamental is open or closed, and makes no network requests of its own.
Requires the Filamental desktop app, with the project opened in it at least once, and Node.js 22 or later.
Open Settings › AI. Under Connect an AI app, Connect sets up Claude Desktop in one click. For any other app, Show setup beside Other AI apps gives the exact block for that app, with the paths for your computer filled in.
Add this to your AI app's MCP settings file (claude_desktop_config.json, .mcp.json for Claude Code, .cursor/mcp.json and so on):
{
"mcpServers": { "filamental": { "command": "npx", "args": ["-y", "filamental-mcp"] } } }
No path is needed. The server works on whichever project Filamental has open, and follows you when you open another, with no restart. To pin it to one project instead, add "--project", "/absolute/path/to/your/project" to args.
| Tool | What it does |
|---|---|
get_structure_report | A short orientation to the whole project: its classifications and connectors, the most connected nodes, the load-bearing ones, separate groups, and nodes that are unconnected or have no notes |
get_context | One node's notes plus its neighbourhood, inlined as one readable markdown document. dry_run sizes it first |
find_path | The shortest chain of connectors between two nodes, by name or UUID, with how each hop reads |
search_nodes | Full-text search across names, notes and property values, with an optional field filter that also works on its own |
get_node | One node's full record, notes included |
get_connections | Every connector on a node, stated from that node's point of view (see Arrow direction) |
get_subgraph | Every node and connector within up to 3 hops of a node |
get_project_info | Node and connector counts, and the classification and connector type names |
list_node_types | The project's classifications (entity types), with their colours and settings |
list_connector_types | The project's connector types, with their colours, directions and weights |
| Tool | What it does |
|---|---|
create_node | Create a node: writes its markdown file and updates the index |
update_node | Change a node. Omitted fields stay as they are; a properties or relationships you pass replaces the whole set |
delete_node | Delete a node and its file |
create_edge | Connect two nodes |
delete_edge | Remove a connector |
extract_section | Move a heading's section out of a node into a node of its own, leaving a [[wikilink]] and a connector behind. dry_run previews it |
inline_section | Fold a node's content back into another: the inverse of extract_section. dry_run previews it |
The content is moved, never copied, so exactly one copy of any text survives.
| Tool | What it does |
|---|---|
read_skill_guide | The full guide to designing a Filamental structure. The server sends the essentials when it connects, so this is for depth |
read_format_reference | The exact on-disk format of node files and the .filamental/ folder |
Every tool carries MCP annotations (readOnlyHint, destructiveHint), so your AI app can tell the read-only tools from the ones that change or delete content.
On Filamental's free plan a project holds 20 nodes, not counting bridges, and that includes nodes an AI creates: create_node says so when a project is full. Pro has no limit.
A connector's direction is one of none, outgoing, incoming or bidirectional. Nothing else is accepted, and an unrecognised value is rejected rather than stored.
On writes (create_edge, create_node, update_node) direction is stated relative to source → target: outgoing draws the arrowhead at the target, incoming draws it back at the source, bidirectional draws both, none is a plain line.
On reads (get_connections) direction is instead stated relative to the node you asked about, because that is what the user sees on screen:
| Field | Meaning |
|---|---|
node | The node you asked about |
other | The node at the far end |
direction | Where the arrowhead is drawn, as seen from node |
stored_on | Which node's file holds the relationship |
This matters because which end of a connector is stored as source is simply whichever end the user happened to drag from, and it's invisible on the graph: an undirected connector looks the same either way round. So the same arrow reads as outgoing from one end and incoming from the other, and get_connections flips it for you. Filtering with direction: "outgoing" gives the edges whose arrow points *away from* the node you asked about. A bidirectional edge matches both outgoing and incoming, since it genuinely points both ways; undirected matches only edges with no arrow.
Use stored_on only if you're editing the underlying markdown file directly. It says nothing about what the arrow does.
filamental-mcp Follow the project Filamental has open
filamental-mcp --project Pin to the project at filamental-mcp --project --db Also override the SQLite index path (for testing)
Every node in a Filamental project is a markdown file with YAML front matter, inside the project folder. The app also keeps a SQLite index of each project for fast search and traversal, stored in the app's own settings folder rather than in the project.
The server opens that index. Read tools query it; write tools update the markdown file and the index together, and keep any front-matter fields they don't own (Obsidian's tags and aliases, say) exactly where they were.
| filamental-mcp | Filamental app | Index schema |
|---|---|---|
| 0.2.6 and later | 0.3.0 and later | v6 |
| 0.2.0 – 0.2.5 | 0.2.x | v5 |
On a schema mismatch the server keeps working where it can, since the index is disposable, and annotates any failing call with which side to update.
MIT. The Filamental desktop app itself is proprietary; its terms are at filamental.space/terms.