🧠 NotebookLM MCP Server

Bridge the Gap Between Google NotebookLM and Your AI Workspace

Stop jumping between browser tabs. NotebookLM MCP brings the full analytical power of Google NotebookLM directly into your local terminal, IDE, and AI assistants like Claude, Cursor, and VS Code.

Manage notebooks, ingest diverse sources, trigger deep research, and generate studio-quality content—all via a single, standardized Model Context Protocol (MCP) interface.

New to MCP? The Model Context Protocol is a standard for connecting LLMs to external data sources and tools. This package speaks MCP over stdio — your AI client (Claude Desktop, Cursor, VS Code, etc.) spawns notebooklm-mcp serve as a subprocess and the two communicate over JSON-RPC. The 32 tools below become callable functions in the model's tool list.

---

🔥 Key Capabilities

• ⚡ Seamless Authentication: Log in once with notebooklm-mcp auth. Our automated CDP-based flow handles secure cookie extraction so you can focus on your data.
• 🔄 Resilient Connectivity: Built-in background session restoration. If your session expires, the server transparently reconnects without breaking your workflow.
• 📂 Universal Ingestion: Instantly add URLs, YouTube transcripts, Google Drive files, or raw text snippets to any notebook.
• 🕵️ Autonomous Research: Harness Google's Deep Research engine. Start a task, poll its progress, and import structured insights directly into your project.
• 🎭 Creative Studio: Programmatically generate Audio Overviews (podcasts), Briefing Docs, Infographics, Slide Decks, and Quizzes from your sources.

---

🚀 Quick Start

1. Installation

Run it instantly with npx:

npx -y @m4ykeldev/notebooklm-mcp serve

Or install globally for better performance:

npm install -g @m4ykeldev/notebooklm-mcp

Developers who want to hack on the source: this repo uses pnpm (pinned via packageManager in package.json). After cloning, run corepack enable && pnpm install. See CONTRIBUTING.md on GitHub for the full dev / release flow.

2. The "One-Click" Login

Say goodbye to manual cookie hunting. Our smart auth flow does the heavy lifting for you.

notebooklm-mcp auth

A secure Chrome window will open. Simply log into your Google account, and we'll handle the rest. Your session is stored locally and securely.

Auth fallbacks if automated Chrome can't run:

notebooklm-mcp auth --manual              # interactive copy/paste from your browser
notebooklm-mcp auth --file tokens.json    # import a previously exported bundle
notebooklm-mcp auth --show-tokens         # verify the cached session

For headless / CI environments, set NOTEBOOKLM_COOKIES (and optionally NOTEBOOKLM_CSRF_TOKEN, NOTEBOOKLM_SESSION_ID) instead of running the auth flow. Token resolution order: env var → ~/.notebooklm-mcp/auth.json → error.

---

🤖 AI Assistant Integration

Pick your client below — every section shows the config file path per OS, a minimal copy-paste block, and the advanced variant with --query-timeout and NOTEBOOKLM_COOKIES env override.

Client	Config file	Format
Claude Desktop	claude_desktop_config.json	JSON
Claude Code (CLI)	~/.claude.json or claude mcp add	JSON / CLI
Codex CLI	~/.codex/config.toml	TOML
OpenAI Agents SDK (Python)	in-code	Python
OpenAI Agents SDK (TypeScript)	in-code	TypeScript
Gemini CLI	~/.gemini/settings.json	JSON
Cursor	~/.cursor/mcp.json	JSON
VS Code (Copilot Chat agent mode)	.vscode/mcp.json	JSON
Windsurf	~/.codeium/windsurf/mcp_config.json	JSON
JetBrains AI Assistant / Junie	~/.junie/mcp/mcp.json	JSON
Zed	~/.config/zed/settings.json	JSON
OpenCode (sst)	opencode.jsonc	JSONC
Cline (VS Code extension)	extension settings UI	JSON
Goose (Block)	~/.config/goose/config.yaml	YAML
5ire	in-app settings	GUI
Aider	not yet supported	—
Generic stdio caller	yours	—

---

Claude Desktop

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve"]
    }
  }
}

<details><summary>With timeout + env override</summary>

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve", "--query-timeout", "180000", "--debug"],
      "env": {
        "NOTEBOOKLM_COOKIES": "SID=...; HSID=...; SSID=...; APISID=...; SAPISID=..."
      }
    }
  }
}

Verify: restart Claude Desktop, click the hammer icon at the bottom-right of the input. Gotchas: absolute paths only; relative paths fail silently on startup.

</details>

---

Claude Code (CLI)

Recommended path is the claude mcp add CLI (writes the JSON for you):

claude mcp add --transport stdio notebooklm -- npx -y @m4ykeldev/notebooklm-mcp serve

<details><summary>Equivalent JSON (project scope, <code>./.mcp.json</code>)</summary>

{
  "mcpServers": {
    "notebooklm": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve"]
    }
  }
}

</details>

<details><summary>With timeout + env override (CLI)</summary>

claude mcp add --transport stdio \
  --env NOTEBOOKLM_COOKIES="SID=...; HSID=...; SSID=...; APISID=...; SAPISID=..." \
  notebooklm -- npx -y @m4ykeldev/notebooklm-mcp serve --query-timeout 180000

Verify: claude mcp list && claude mcp get notebooklm, or /mcp inside a session. Gotchas: all options before the server name; use -- to separate options from the command. Project-scoped .mcp.json needs interactive approval on first sight.

</details>

---

Codex CLI

• macOS: ~/.codex/config.toml
• Linux: ~/.config/codex/config.toml
• Windows: %APPDATA%\codex\config.toml

[mcp_servers.notebooklm]
command = "npx"
args = ["-y", "@m4ykeldev/notebooklm-mcp", "serve"]

<details><summary>With timeout + env override</summary>

[mcp_servers.notebooklm]
command = "npx"
args = ["-y", "@m4ykeldev/notebooklm-mcp", "serve", "--query-timeout", "180000"]
env = { NOTEBOOKLM_COOKIES = "SID=...; HSID=...; SSID=...; APISID=...; SAPISID=..." }

Verify: codex --list-tools should list the NotebookLM tools. Gotchas: Codex CLI's MCP schema is still being formalized — double-check against the latest openai/codex README.

</details>

---

OpenAI Agents SDK — Python

Wire it in code (no config file):

from agents.mcp import MCPServerStdio

async with MCPServerStdio(
    name="NotebookLM",
    params={
        "command": "npx",
        "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve"],
    },
) as server:
    tools = await server.list_tools()
    print([t.name for t in tools])  # 32 tools

<details><summary>With timeout + env override</summary>

async with MCPServerStdio(
    name="NotebookLM",
    params={
        "command": "npx",
        "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve", "--query-timeout", "180000"],
        "env": {"NOTEBOOKLM_COOKIES": "SID=...; HSID=...; SSID=...; APISID=...; SAPISID=..."},
    },
) as server:
    ...

</details>

---

OpenAI Agents SDK — TypeScript

import { MCPServerStdio } from "@openai/agents";

const server = new MCPServerStdio({
  command: "npx",
  args: ["-y", "@m4ykeldev/notebooklm-mcp", "serve"],
});

await server.connect();
const tools = await server.listTools();
console.log(tools.map((t) => t.name)); // 32 tools

<details><summary>With timeout + env override</summary>

const server = new MCPServerStdio({
  command: "npx",
  args: ["-y", "@m4ykeldev/notebooklm-mcp", "serve", "--query-timeout", "180000"],
  env: { NOTEBOOKLM_COOKIES: "SID=...; HSID=...; SSID=...; APISID=...; SAPISID=..." },
});

</details>

---

Gemini CLI

~/.gemini/settings.json (user-global) or .gemini/settings.json (per-project).

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve"]
    }
  }
}

<details><summary>With timeout + env override (Gemini supports <code>$VAR</code> expansion)</summary>

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve"],
      "timeout": 180000,
      "env": {
        "NOTEBOOKLM_COOKIES": "$NOTEBOOKLM_COOKIES"
      }
    }
  }
}

Verify: gemini mcp list, or /mcp in a session. Gotchas: undefined $VAR resolves to empty string — pre-export them in your shell.

</details>

---

Cursor

• macOS / Linux: ~/.cursor/mcp.json (global) or .cursor/mcp.json (per-project)
• Windows: %APPDATA%\Cursor\mcp.json

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve"]
    }
  }
}

<details><summary>With timeout + env override</summary>

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve", "--query-timeout", "180000"],
      "env": {
        "NOTEBOOKLM_COOKIES": "SID=...; HSID=...; SSID=...; APISID=...; SAPISID=..."
      }
    }
  }
}

Verify: Cursor Settings → MCP should show notebooklm in green, or MCP: View Server Status in the Command Palette. Gotchas: Cursor only loads MCP servers at startup — fully quit and relaunch after edits. Soft ~40-tool ceiling across all enabled servers combined.

</details>

---

VS Code (Copilot Chat agent mode)

.vscode/mcp.json (per-project). Top-level key is servers (not mcpServers — Microsoft renamed this).

{
  "servers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve"]
    }
  }
}

<details><summary>With timeout + env override</summary>

{
  "servers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve", "--query-timeout", "180000"],
      "env": {
        "NOTEBOOKLM_COOKIES": "SID=...; HSID=...; SSID=...; APISID=...; SAPISID=..."
      }
    }
  }
}

Verify: Command Palette → MCP: Open User Configuration. Ask Copilot Chat in agent mode to list NotebookLM notebooks.

</details>

---

Windsurf

• macOS / Linux: ~/.codeium/windsurf/mcp_config.json
• Windows: %APPDATA%\Codeium\Windsurf\mcp_config.json

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve"]
    }
  }
}

<details><summary>With timeout + env override (uses Windsurf's <code>${env:VAR}</code> interpolation)</summary>

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve", "--query-timeout", "180000"],
      "env": {
        "NOTEBOOKLM_COOKIES": "${env:NOTEBOOKLM_COOKIES}"
      }
    }
  }
}

Verify: open the Cascade sidebar → MCP settings → confirm notebooklm is active. Gotchas: prefer ${env:VAR} / ${file:/path} interpolation over inlining cookies; Windsurf substitutes at server-launch time so secrets stay out of the config file.

</details>

---

JetBrains AI Assistant / Junie

• macOS / Linux: ~/.junie/mcp/mcp.json (user-global) or .junie/mcp/mcp.json (per-project)
• Windows: %APPDATA%\JetBrains\Junie\mcp.json

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve"]
    }
  }
}

<details><summary>With timeout + env override</summary>

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve", "--query-timeout", "180000"],
      "env": {
        "NOTEBOOKLM_COOKIES": "SID=...; HSID=...; SSID=...; APISID=...; SAPISID=..."
      }
    }
  }
}

Verify: Settings (Ctrl+Alt+S) → Tools → Junie → MCP Settings — notebooklm should appear in the discovered list.

</details>

---

Zed

~/.config/zed/settings.json — the relevant key is context_servers (Zed's MCP equivalent).

{
  "context_servers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve"]
    }
  }
}

<details><summary>With timeout + env override</summary>

{
  "context_servers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve", "--query-timeout", "180000"],
      "env": {
        "NOTEBOOKLM_COOKIES": "SID=...; HSID=...; SSID=...; APISID=...; SAPISID=..."
      }
    }
  }
}

Verify: Zed's AI Agent Panel lists notebooklm as an available context source.

</details>

---

OpenCode (sst)

opencode.jsonc (project root) or ~/.config/opencode/config.json.

{
  "mcp": {
    "servers": {
      "notebooklm": {
        "command": "npx",
        "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve"]
      }
    }
  }
}

<details><summary>With timeout + env override</summary>

{
  "mcp": {
    "servers": {
      "notebooklm": {
        "command": "npx",
        "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve", "--query-timeout", "180000"],
        "env": {
          "NOTEBOOKLM_COOKIES": "SID=...; HSID=...; SSID=...; APISID=...; SAPISID=..."
        }
      }
    }
  }
}

Verify: start OpenCode and ask "List my NotebookLM notebooks." — the model should call notebook_list. Gotchas: OpenCode's MCP schema is still evolving; confirm against the OpenCode docs if startup fails.

</details>

---

Cline (VS Code extension)

Managed via Cline's MCP settings UI inside VS Code. Underlying file (don't edit by hand): ~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json.

1. Open the Cline panel → click the MCP icon → Configure MCP Servers.
2. Paste:

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve"]
    }
  }
}

3. Save. Cline restarts the server automatically.

<details><summary>With timeout + env override</summary>

{
  "mcpServers": {
    "notebooklm": {
      "command": "npx",
      "args": ["-y", "@m4ykeldev/notebooklm-mcp", "serve", "--query-timeout", "180000"],
      "env": {
        "NOTEBOOKLM_COOKIES": "SID=...; HSID=...; SSID=...; APISID=...; SAPISID=..."
      }
    }
  }
}

Verify: ask Cline "List my NotebookLM notebooks." — the tool-call panel shows notebook_list.

</details>

---

Goose (Block)

~/.config/goose/config.yaml (or via goose configure). Goose calls MCP servers "extensions".

extensions:
  notebooklm:
    type: stdio
    command: npx
    args:
      - "-y"
      - "@m4ykeldev/notebooklm-mcp"
      - serve

<details><summary>With timeout + env override</summary>

extensions:
  notebooklm:
    type: stdio
    command: npx
    args:
      - "-y"
      - "@m4ykeldev/notebooklm-mcp"
      - serve
      - "--query-timeout"
      - "180000"
    env:
      NOTEBOOKLM_COOKIES: "SID=...; HSID=...; SSID=...; APISID=...; SAPISID=..."

Verify: goose configure → confirm the extension is enabled, then goose session and ask "List my NotebookLM notebooks."

</details>

---

5ire

GUI app (no user-editable config file):

1. Open 5ire → Settings (Mod+K → Providers).
2. Click Add MCP Server.
3. Name: notebooklm
4. Type: Stdio
5. Command: npx
6. Args: -y @m4ykeldev/notebooklm-mcp serve (add --query-timeout 180000 if needed)
7. Env vars (optional): NOTEBOOKLM_COOKIES=SID=...; HSID=...; SSID=...; APISID=...; SAPISID=...
8. Save and restart.

Verify: ask in chat "List my NotebookLM notebooks." — tool-call panel shows notebook_list.

---

Aider

Aider does not yet have first-class MCP-server client integration. Workarounds: run notebooklm-mcp serve separately and pipe responses, or use the generic stdio caller inside a small wrapper script. Track upstream support via the Aider Discord.

---

Generic stdio caller

If your client speaks the MCP protocol over stdio but isn't listed above, every config above reduces to the same primitive:

spawn:   npx -y @m4ykeldev/notebooklm-mcp serve
stdio:   parent <- stdout (JSON-RPC responses) | parent -> stdin (JSON-RPC requests)
env:     (optional)
  NOTEBOOKLM_COOKIES=...        # skip the auth flow in CI
  NOTEBOOKLM_CSRF_TOKEN=...
  NOTEBOOKLM_SESSION_ID=...
flags:   (optional)
  --query-timeout <ms>          # per-RPC timeout, default 120000
  --debug                       # verbose stderr logging

On connection the server advertises 32 tools — see the Complete Tool Reference below.

---

Verify any wiring

After restarting your client, ask:

"List my NotebookLM notebooks."

If the model invokes notebook_list and returns a table of titles, you're connected.

---

💬 Example Prompts

Once wired up, your AI can drive NotebookLM end-to-end with natural language. The model picks the right tool from the 32 below.

Goal	Sample prompt	Tools the model will call
Inventory	"Show me every NotebookLM project I own"	notebook_list
Start a project	"Create a notebook called 'Q3 Earnings' and add the AAPL 10-Q PDF at https://…"	notebook_create, notebook_add_url
Grounded Q&A	"From the AAPL notebook, what is the year-over-year services revenue change?"	notebook_query
Multi-source brief	"In my 'Climate Policy' notebook, generate a one-page briefing doc focused on IRA tax credits"	report_create, studio_status
Studio podcast	"Make a 10-min audio overview of my 'AI Safety Reading' notebook"	audio_overview_create, studio_status
Deep Research → import	"Run deep research on 'kelp aquaculture in Maine' and import the findings into my 'Climate' notebook"	research_start, research_status, research_import
Cleanup	"Delete the audio overview I generated yesterday in 'AI Safety Reading'"	studio_status, studio_delete

🛠 Complete Tool Reference (32)

Every tool is designed to work seamlessly within your AI's context window.

📔 Notebook Management

Tool	Description
notebook_list	Get an overview of all your notebooks, including titles, source counts, and ownership metadata.
notebook_create	Create a new NotebookLM project instantly from your terminal or AI assistant.
notebook_get	Retrieve deep metadata and a full list of sources for a specific notebook.
notebook_describe	Get a high-level, AI-generated summary of everything inside a notebook.
notebook_rename	Update the title of an existing notebook.
notebook_delete	Permanently remove a notebook (requires explicit confirmation).

📄 Source Ingestion & Management

Tool	Description
notebook_add_url	Add any website or YouTube video as a source. Transcripts are automatically handled.
notebook_add_text	Ingest raw text snippets or local file contents directly into your project.
notebook_add_drive	Connect and import documents, sheets, or slides from your Google Drive.
source_describe	Get detailed AI analysis, summaries, and key topics for any individual source.
source_get_content	Extract the full underlying text of a source for processing by other AI tools.
source_list_drive	List all Drive-based sources and check if they are up-to-date with the original files.
source_sync_drive	Sync selected Google Drive sources to pull the latest changes into NotebookLM.
source_delete	Remove a specific source from your notebook.

🔬 Research & Deep Analysis

Tool	Description
research_start	Launch an autonomous research task using Google's engine (Web or Drive sources).
research_status	Track the progress of active research tasks and view discovered insights.
research_import	Instantly import the findings of a research task as new sources in your notebook.
notebook_query	Ask complex, grounded questions. Answers are cited directly from your sources.
chat_configure	Fine-tune your AI's behavior by setting specific goals or preferred response lengths.

🎬 Studio (AI Content Generation)

Tool	Description
audio_overview_create	Transform your notebook's sources into a professional, podcast-style audio discussion.
video_overview_create	Generate a structured video explainer based on your project data.
report_create	Create professional Briefing Docs, Study Guides, or Blog Posts tailored to your sources.
slide_deck_create	Turn your research into a presenter-ready slide deck automatically.
infographic_create	Visualize complex data and relationships with an AI-generated infographic.
flashcards_create	Generate interactive study flashcards to master your notebook's content.
quiz_create	Create a comprehensive quiz to test knowledge grounded in your provided sources.
data_table_create	Extract and organize information into a structured, downloadable data table.
mind_map_create	Build a visual mind map connecting the core concepts of your notebook.
studio_status	Check the generation status of your Studio artifacts and get download links.
studio_delete	Clean up your workspace by deleting old Studio artifacts.

🔑 Authentication Helpers

Tool	Description
refresh_auth	Manually trigger a session refresh if you encounter connection issues.
save_auth_tokens	Manually save cookie data (legacy fallback method).

---

🎛 CLI Reference

notebooklm-mcp serve [--query-timeout <ms>] [--debug]
notebooklm-mcp auth  [--manual] [--file <path>] [--show-tokens]
notebooklm-mcp --version

Flag	Default	Description
serve --query-timeout	120000	Per-RPC timeout in ms. Bump for large studio jobs or slow research.
serve --debug	off	Verbose stderr logging of every RPC + retry.
auth --manual	off	Skip Chrome automation; paste cookies yourself.
auth --file <path>	—	Import a previously exported tokens JSON.
auth --show-tokens	—	Print the cached session's metadata (cookie names, age, CSRF/SID presence — never the secret values).

---

🩹 Troubleshooting

Symptom	Likely cause	Fix
"Could not find Google Chrome or Chromium"	No Chrome/Chromium on PATH	Install Chrome or run notebooklm-mcp auth --manual
"Authentication expired" inside the AI session	Cookies stale	The server auto-refreshes once; if it can't, re-run notebooklm-mcp auth
Tool calls hang past 2 min	Big studio job over default timeout	Restart server with --query-timeout 300000
"file_path … outside the allowed roots"	notebook_add_text got a path outside cwd / tmp	Copy the file into your working directory or pass content inline
MCP client reports server crashed on startup	dist/cli.js missing (dev clone)	pnpm install && pnpm run build
Multiple Chrome windows pop up on concurrent failures	Older version without single-flight mutex	Upgrade to ≥ v0.2.5

---

💡 Pro Tips

• Custom Timeouts: Working with massive sources? Increase the timeout: notebooklm-mcp serve --query-timeout 180000
• Check Connections: Use notebooklm-mcp auth --show-tokens to verify your session validity without exposing the secrets.
• CI / headless: Set NOTEBOOKLM_COOKIES (plus NOTEBOOKLM_CSRF_TOKEN, NOTEBOOKLM_SESSION_ID) to skip the browser flow entirely.

---

🛡 Security & Privacy

• Local Storage with hardened perms. Tokens live at ~/.notebooklm-mcp/auth.json (mode 0600) inside ~/.notebooklm-mcp/ (mode 0700). Nothing leaves your machine except calls to Google.
• Path-traversal guard. notebook_add_text rejects file_path arguments outside the working directory or the OS temp directory — a hostile MCP prompt cannot ask the server to read your ~/.ssh/id_rsa.
• Loopback-only Chrome DevTools. The automated auth flow launches Chrome with an OS-assigned ephemeral port bound to 127.0.0.1 — no fixed-port squatting, no LAN exposure.
• Single-flight auth refresh. Concurrent requests that hit an expired session share one refresh promise instead of each spawning their own Chrome.
• Supply-chain gated releases. Every publish runs gitleaks → osv-scanner → pnpm audit (high+) → npm audit signatures (Sigstore) → lockfile registry pinning → publish-manifest preview before pnpm publish ships via OIDC trusted publishers. See CONTRIBUTING.md → Releasing on GitHub.
• Test coverage: 100% statements / branches / functions / lines, enforced by CI.
• Unofficial Tool: This project is an independent community effort and is not affiliated with Google. It interfaces with internal endpoints and may be affected by changes to the NotebookLM web platform.

📄 License

Open-source and available under the MIT License.

---

Crafted with precision for the AI-first developer. Part of the Model Context Protocol ecosystem.