Fivetran Mcp — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Fivetran Mcp (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
25 tools. 161 endpoints. Zero bloat.
A hybrid Fivetran MCP server built on the architecture Cloudflare pioneered for their own API: instead of drowning the model in dozens of tool definitions, we give it 23 fast native tools for the data engineering workflow you actually use, plus two universal tools that unlock the entire Fivetran REST API on demand.
Existing Fivetran MCP servers expose 13–20+ individual tools, each one permanently loaded into the LLM's context window. They still don't cover the full API — no transformations, no teams, no webhooks, no schema management depth. So you get the worst of both worlds: context overhead without completeness.
This is the context flooding problem. Cloudflare ran the math on their own API (2,500+ endpoints) and found that exposing everything as native MCP tools would consume 1.17 million tokens per turn. Their solution was radical: collapse the entire API surface into just two tools — search and execute — and let the model discover what it needs on the fly. They called it Code Mode, and it reduced the footprint to ~1,000 tokens.
Pure Code Mode is elegant, but it has a tradeoff. For the stuff you do every single day — check sync status, trigger a sync, list connections, pause a connector — forcing the model to search the API catalog first adds an unnecessary round trip. You already know what you want. The model should too.
So we built a hybrid:
Layer 1: 23 native tools for the complete data engineering workflow. These are purpose-built, zero-overhead, and handle the 80% case. Checking sync status, triggering a resync, managing schemas — one tool call, done. No searching, no discovering, no extra turns.
Layer 2: `search` + `execute` for everything else. An embedded catalog of all 161 Fivetran API endpoints, generated from the official OpenAPI spec. The model searches to discover endpoints, then executes to call them. Private links, proxy agents, system keys, custom connector SDKs — it's all there without adding a single extra tool definition.
The result: 25 tool schemas in your context window instead of an incomplete subset. Fast for the common case, omnipotent for the edge case.
| Other Fivetran MCPs | fivetran-mcp | |
|---|---|---|
| Tools in context | 13–20+ | 25 |
| API coverage | Partial | Full (161 endpoints) |
| Token cost per turn | High (all schemas always loaded) | Minimal |
| Common tasks | Same overhead as rare ones | Optimized native tools |
| New Fivetran endpoints | Requires code changes | Already covered via execute |
The secret is only shown once. Store it somewhere safe.
Add to your ~/.claude.json (or project-level .claude.json) under mcpServers:
{
"mcpServers": {
"fivetran": {
"type": "stdio",
"command": "npx",
"args": ["-y", "fivetran-mcp"],
"env": {
"FIVETRAN_API_KEY": "your-api-key",
"FIVETRAN_API_SECRET": "your-api-secret"
}
}
}
}Restart Claude Code for the server to connect. You'll see fivetran in your MCP server list, and the tools will appear as mcp__fivetran__list_connections, mcp__fivetran__search, etc.
Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"fivetran": {
"command": "npx",
"args": ["-y", "fivetran-mcp"],
"env": {
"FIVETRAN_API_KEY": "your-api-key",
"FIVETRAN_API_SECRET": "your-api-secret"
}
}
}
}Restart Claude Desktop. The Fivetran tools will appear in the tools menu (hammer icon).
The config pattern is the same — npx -y fivetran-mcp as the command, with your API key and secret in the env block. Consult your client's MCP documentation for where to place the config.
If you cloned the repo instead of using npx:
{
"mcpServers": {
"fivetran": {
"command": "node",
"args": ["/path/to/fivetran-mcp/dist/index.js"],
"env": {
"FIVETRAN_API_KEY": "your-api-key",
"FIVETRAN_API_SECRET": "your-api-secret"
}
}
}
}These 23 tools cover the complete data engineering lifecycle — no searching required:
| Tool | What it does |
|---|---|
list_connections | List all connections with sync status, optionally filtered by group |
get_connection_details | Get full config, status, schedule for a specific connection |
create_connection | Create a new connector (use get_connector_metadata to discover types) |
update_connection | Update connection config, schedule, or settings |
delete_connection | Permanently delete a connection |
trigger_sync | Trigger an immediate sync without waiting for schedule |
pause_connection | Pause a connection's sync schedule |
resume_connection | Resume a paused connection |
| Tool | What it does |
|---|---|
get_connection_schema | Get schema/table/column config with enabled/disabled state |
update_connection_schema | Enable/disable schemas, tables, and columns |
reload_schema | Refresh schema from source (detect new tables/columns) |
| Tool | What it does |
|---|---|
get_sync_status | Current sync state, last sync time, warnings, failures |
get_table_status | Per-table sync status flattened into a readable list |
get_sync_logs | Connection state and sync history |
| Tool | What it does |
|---|---|
list_groups | List all groups (workspaces) |
get_group_connectors | List all connections within a group |
list_destinations | List all destinations with config |
test_destination | Run destination connectivity tests |
| Tool | What it does |
|---|---|
list_users | List all account users |
invite_user | Invite a new user with role |
list_teams | List all teams |
| Tool | What it does |
|---|---|
resync_tables | Force historical resync of specific tables (not the whole connection) |
get_connector_metadata | Discover available connector types and their required config |
1. list_groups() → find your workspace
2. get_group_connectors(group_id) → see all connections
3. get_sync_status(connection_id) → check if sync succeeded
4. get_table_status(connection_id) → drill into table-level status
5. trigger_sync(connection_id) → force a sync if needed
6. get_connection_schema(connection_id) → inspect what's being syncedresync_tables(
connection_id: "spoke_foolish",
tables: { "public": ["users", "orders", "products"] }
)For anything beyond the 23 native tools — private links, proxy agents, system keys, custom connector SDKs, HVR registrations, and more — use search and execute.
Call with no arguments to see the full API map:
> search()
Fivetran REST API — 161 endpoints across 21 categories
Account Management (1) — Account-level info and settings
Certificate Management (17) — SSL certificate and fingerprint approval
Connection Management (11) — Connectors — create, configure, sync, pause, resume, delete
Connection Schema Management (11) — Schema, table, and column configuration
Team Management (21) — Teams — membership, groups, connections, permissions
User Management (16) — Users — invite, modify, delete, memberships
...Narrow it down:
> search(tag: "Webhook Management")
> search(query: "schema reload")
> search(query: "proxy agent", method: "POST")> execute(method: "GET", path: "/account/info")
{ "account_id": "...", "account_name": "..." }
> execute(method: "GET", path: "/roles")
> execute(method: "POST", path: "/webhooks", body: {
"url": "https://example.com/webhook",
"events": ["sync_end"],
"active": true
})| Category | Endpoints | Description |
|---|---|---|
| Account Management | 1 | Account-level info and settings |
| Certificate Management | 17 | SSL certificate and fingerprint approval |
| Connection Management | 11 | Connectors — create, configure, sync, pause, resume, delete |
| Connection Schema Management | 11 | Schema, table, and column configuration |
| Connector Metadata | 2 | Available connector types and config schemas |
| Connector SDK Package Resource | 6 | Custom connector SDK management |
| Destination Management | 6 | Data warehouse and lake destinations |
| Group Management | 11 | Groups — connectors, users, service accounts |
| HVR Registrations Management | 1 | HVR replication registrations |
| Hybrid Deployment Agent Management | 6 | On-premises deployment agents |
| Log Service Management | 10 | Sync logs and log service configuration |
| Private Link Management | 5 | Private networking (AWS/Azure/GCP) |
| Proxy Agent Management | 6 | Proxy agent configuration |
| Public Endpoints | 1 | Public API information |
| Role Management | 1 | Roles and permissions |
| System Key Management | 6 | System API key management |
| Team Management | 21 | Teams — membership, groups, connections, permissions |
| Transformation Management | 10 | dbt transformations — create, run, manage |
| Transformation Projects Management | 6 | dbt project management |
| User Management | 16 | Users — invite, modify, delete, memberships |
| Webhook Management | 7 | Webhooks — create, test, manage notifications |
git clone https://github.com/livemau5/fivetran-mcp.git
cd fivetran-mcp
npm install
npm run build
# Regenerate the API catalog from the latest Fivetran spec
npm run generate-catalog
# Run in development mode
FIVETRAN_API_KEY=your-key FIVETRAN_API_SECRET=your-secret npm run devsrc/
index.ts Entry point — server setup, tool registration, stdio transport
types.ts CatalogEntry interface
utils.ts Auth header building, URL construction, response formatting
api-catalog.ts Auto-generated catalog of all 161 endpoints
tools/
native.ts 23 native tools for the data engineering workflow
search.ts Search tool — text/tag/method filtering over the catalog
execute.ts Execute tool — HTTP client with automatic Basic Auth
scripts/
generate-catalog.ts Parses official Fivetran OpenAPI spec into api-catalog.tsMIT
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.