@savantoai/mcp-server
MCP serverDatabases & dataThis app connects your AI to your Savanto store so you can run it by chat. Once added, you can handle your catalog, content, prompts, and analytics in conversation instead of switching between tools.
Available today. Use it from your connected AI after setup.
No other account needed.
After adding it, start a chat with your AI and ask it to show your catalog or recent analytics. From there, you can make changes or ask questions about your store in plain language.
Then ask your AI: use @savantoai/mcp-server
What your AI can do with it
- Update your store catalog through chat
- Edit your store's content in conversation
- Manage the prompts your store uses
- Check store analytics without leaving the chat
From the project's README
As published by savantoai/mcp-server in README.md.
A local Model Context Protocol server that exposes your Savanto AI workspace to Claude, ChatGPT, Cursor, and any other MCP-compatible client — so you can configure, populate, and operate your store's AI assistant by talking to your own AI, instead of clicking through a dashboard.
What it does
Once configured, your agent gains a curated set of tools that mirror the Savanto REST API, spanning the full configure → observe → refine loop:
| Category | Representative tools | Scope |
|---|---|---|
| Workspaces | list_workspaces, create_workspace, update_workspace, delete_workspace | tenant:admin |
| Configuration | get_workspace_settings, update_workspace_settings, custom-domain CRUD, discover_tools, generate_domain_config, validate_custom_domain, test_domain_connection, generate_color_scheme, chat/search widget config | config:admin |
| Content | upsert_product/upsert_post (+ bulk_*, list_*, get_*, patch_*, delete_*) | admin:products, admin:posts |
| Taxonomies | upsert_taxonomy, bulk_upsert_taxonomies, list/get/delete_taxonomy | admin:taxonomies |
| Prompts | upsert_prompt, list_prompts, search_prompts, delete_prompt (+ bulk) | admin:prompts, prompts:read |
| Webhooks | create_webhook, list/get/update/delete_webhook, test_webhook, get_webhook_stats | admin:webhooks |
| Crawl | start_crawl, get_crawl_status/history/config, update_crawl_config | admin:posts |
| Search | search_products, search_posts | search:products, search:posts |
| Analytics | get_search_analytics, get_chat_analytics, get_feedback_analytics, search_search_logs, list_feedback | tenant:admin, feedback:admin |
| Threads | search_threads, get_thread, get_thread_messages, get_thread_analytics, delete_thread, bulk_delete_threads | threads:admin |
| Chat | chat | chat |
| Diagnostics | whoami, get_tenant_usage | (none) / tenant:admin |
Two things keep the surface safe and legible to clients:
- Scope-gated at startup — the server probes
/tenant/whoamiand only registers tools your key can actually use. An agent is never shown a tool it would get a 403 for, and a publishable widget key sees almost nothing. - Annotated — every tool carries MCP hints (
readOnlyHint,destructiveHint,idempotentHint) so clients can auto-approve safe reads and flag destructive writes; deletes additionally require an explicitconfirm: true.
The server also exposes Skills (MCP prompts) — step-by-step playbooks for common multi-tool workflows:
onboard-store-end-to-end– create a workspace, ingest content, configure behaviour + branding, smoke-testonboard-wordpress/onboard-shopify– platform-specific onboarding walkthroughsconfigure-chat– tune persona, special instructions, and handoff rulesconfigure-custom-domain– wire a custom capability (order tracking, account lookup) to MCP servers / REST APIsaudit-and-improve– the observe→refine loop: find failing chats / zero-result searches / negative feedback and fix themdebug-empty-search– diagnose why a product search returns no hitsmigrate-from-competitor– bulk-import from another chat vendor's export
Requirements
- Node.js 20 or later
- A secret Savanto API key (starts with
if_sk_…). Create one in the API Keys page of your dashboard.Publishable keys (
if_pk_…) are client-side and cannot provision workspaces — the server will refuse to start with one.
Quick start
No global install needed — run it with npx:
export SAVANTO_API_KEY=if_sk_your_key_here
npx -y @savantoai/mcp-server
Point to a non-production cloud (staging, local dev):
export SAVANTO_API_URL=http://localhost:3001
Remote server (preview)
In addition to the local stdio server above, the same tool surface can run as a
hosted HTTP server so clients connect to a URL instead of spawning npx —
no local Node, no per-machine config. This is the path toward one-click
"Connect to Claude/ChatGPT" (OAuth) onboarding; today it accepts your secret key
as a Bearer token.
# Each client authenticates per-request — there is NO server-wide key.
SAVANTO_API_URL=https://api.savanto.ai PORT=8080 npx -y -p @savantoai/mcp-server savanto-mcp-http
The server mounts the MCP endpoint at /mcp and a liveness probe at /healthz.
Clients send their key as Authorization: Bearer if_sk_…; the tool surface is
scope-gated to that key's tenant, exactly as in the stdio server. Point an MCP
client that supports remote (Streamable HTTP) servers at
https://your-host/mcp with that bearer token.
Auth is currently the raw secret key. A future release replaces it with OAuth-issued, tenant-scoped tokens so customers can connect with zero key handling — the transport and tool layer are unchanged by that swap.
Client configuration
Claude Desktop
Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"savanto": {
"command": "npx",
"args": ["-y", "@savantoai/mcp-server"],
"env": {
"SAVANTO_API_KEY": "if_sk_your_key_here"
}
}
}
}
Restart Claude Desktop. You should see a hammer/tool icon in the message bar; the Savanto tools are listed there.
Cursor
In Cursor settings → Features → Model Context Protocol → Add new MCP server:
{
"savanto": {
"command": "npx",
"args": ["-y", "@savantoai/mcp-server"],
"env": { "SAVANTO_API_KEY": "if_sk_your_key_here" }
}
}
Cline / Roo / other VS Code agents
Add to the extension's MCP config (usually a JSON file under ~/.cline or similar):
{
"mcpServers": {
"savanto": {
"command": "npx",
"args": ["-y", "@savantoai/mcp-server"],
"env": { "SAVANTO_API_KEY": "if_sk_your_key_here" }
}
}
}
OpenAI Agents / Responses API
from openai import OpenAI
from mcp import StdioServerParameters
server = StdioServerParameters(
command="npx",
args=["-y", "@savantoai/mcp-server"],
env={"SAVANTO_API_KEY": "if_sk_your_key_here"},
)
Local MCP Inspector (for debugging)
npx @modelcontextprotocol/inspector npx @savantoai/mcp-server
The Inspector gives you a web UI to list tools, call them directly, and watch request/response payloads — great for confirming your key is wired correctly before handing the server to an agent.
Example prompts
Once the server is registered in your MCP client, try:
"Set up a new Savanto workspace for
acme-store, crawlhttps://acme.test, give it an outdoor-adventure tone, and brand the widget around#0a7d2c." (end-to-end onboarding)
"Look at
acme-store's last 30 days — what are visitors searching for that returns nothing, and which conversations went unresolved? Then add content to fix the top few." (the observe→refine loop)
"Add an order-tracking capability to
acme-storebacked by our MCP server athttps://mcp.acme.test/orders, validate it, and test it before enabling." (custom domain)
"Why did this conversation get a thumbs-down?" — pull
list_feedback, read the thread, and propose a fix.
The agent picks the right tools automatically (and clients can auto-approve the read-only ones). You can also invoke a Skill explicitly — e.g. in Claude Desktop, /onboard-store-end-to-end or /audit-and-improve kicks off that full playbook.
Environment variables
| Variable | Default | Purpose |
|---|---|---|
SAVANTO_API_KEY | (required) | Your secret API key (if_sk_…). |
SAVANTO_API_URL | https://api.savanto.ai | Override for staging / local dev. |
Security
- Always use separate API keys per agent / machine — so you can revoke one without affecting the others. The API Keys page tracks the last-used timestamp of each key.
- Keys are passed via environment variables, never logged. The server prints a one-line identity banner on startup (to stderr) showing the tenant id and scope list — no secrets.
- The server runs over stdio and never opens a network port. It only speaks to the Savanto API host you point it at.
delete_workspacerequires an explicitconfirm: trueparameter in the tool call — a safety gate against hallucinated destructive operations.
Local development
From the repo root:
npm install
npm run build --workspace=@savantoai/mcp-server
SAVANTO_API_KEY=if_sk_… SAVANTO_API_URL=http://localhost:3001 node sdks/mcp/dist/stdio.js
Run the tests:
npm run test --workspace=@savantoai/mcp-server
License
MIT. See LICENSE.
Signals
- Last commit
- Jun 2026
Advanced
- Delivery
- mcp-server MCP server → your ahel gateway (mcp.ahel.ai) → every connected AI client.
- Catalog kind
- mcp-server
- Gateway key
io-github-savantoai-mcp-server- Source
- github.com/savantoai/mcp-server
- Hosted endpoint
https://mcp.savanto.ai/mcp