Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Description from the maker (original English)
Search, read and build your Filamental knowledge graph. Local-first: notes stay on your computer.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
You need
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Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"filamental": {
"command": "npx",
"args": [
"-y",
"filamental-mcp"
]
}
}
}Run this in your terminal
claude mcp add filamental -- npx -y filamental-mcpPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"filamental": {
"command": "npx",
"args": [
"-y",
"filamental-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"filamental": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"filamental-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
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These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The registry name was verified with the GitHub account Scottnine, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.2.17) reached the registry 9 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Context7, Codebase Memory, Exa
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
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.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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.