Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Description from the maker (original English)
Read-only-by-default MCP for Datanika: browse data, run dbt transforms, manage ELT pipelines.
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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.
DATANIKA_URL, DATANIKA_API_KEY, DATANIKA_ALLOW_WRITEValues marked secret are keys to your account. Never share them or paste them into a chat.
You need
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Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"datanika": {
"command": "uvx",
"args": [
"datanika-mcp"
],
"env": {
"DATANIKA_URL": "your-value",
"DATANIKA_API_KEY": "your-value",
"DATANIKA_ALLOW_WRITE": "your-value"
}
}
}
}Run this in your terminal
claude mcp add datanika --env DATANIKA_URL=your-value --env DATANIKA_API_KEY=your-value --env DATANIKA_ALLOW_WRITE=your-value -- uvx datanika-mcpFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"datanika": {
"command": "uvx",
"args": [
"datanika-mcp"
],
"env": {
"DATANIKA_URL": "your-value",
"DATANIKA_API_KEY": "your-value",
"DATANIKA_ALLOW_WRITE": "your-value"
}
}
}
}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": {
"datanika": {
"type": "stdio",
"command": "uvx",
"args": [
"datanika-mcp"
],
"env": {
"DATANIKA_URL": "your-value",
"DATANIKA_API_KEY": "your-value",
"DATANIKA_ALLOW_WRITE": "your-value"
}
}
}
}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.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
DATANIKA_URL envDATANIKA_ALLOW_WRITE envThese 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 6, 2026
The registry name was verified against the domain datanika.io (the registry checks domain ownership)
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.
You need to fill in: DATANIKA_API_KEY
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 (0.2.0) reached the registry 4 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
In the same category: Spec Driven Development, Remote Desktop Commander, Zoo MCP
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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
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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.
mcp-name: io.datanika/datanika-mcp
MCP server for Datanika — browse connections, preview data, compile and validate dbt transformations, monitor runs, and manage pipelines from Claude Desktop.
Read-only by default. Pass --allow-write to enable creating resources and triggering pipeline runs.
# From PyPI (recommended)
uvx datanika-mcp --help
uvx --from "git+https://github.com/datanika-io/datanika-core#subdirectory=datanika-mcp" datanika-mcp --help
Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows):
{
"mcpServers": { "datanika": { "command": "uvx", "args": [ "--from", "git+https://github.com/datanika-io/datanika-core#subdirectory=datanika-mcp", "datanika-mcp", "--url", "https://app.datanika.io", "--api-key", "YOUR_API_KEY" ] } } }
{
"mcpServers": { "datanika": { "command": "uvx", "args": [ "--from", "git+https://github.com/datanika-io/datanika-core#subdirectory=datanika-mcp", "datanika-mcp", "--url", "https://app.datanika.io", "--api-key", "YOUR_API_KEY", "--allow-write" ] } } }
You can also configure via environment variables:
{
"mcpServers": { "datanika": { "command": "uvx", "args": [ "--from", "git+https://github.com/datanika-io/datanika-core#subdirectory=datanika-mcp", "datanika-mcp" ], "env": { "DATANIKA_URL": "https://app.datanika.io", "DATANIKA_API_KEY": "YOUR_API_KEY", "DATANIKA_ALLOW_WRITE": "true" } } } }
| Tool | Description |
|---|---|
get_agent_tiers | Get the 5-tier agent capability stack |
get_connection_types | List supported connection types with config schemas |
list_connections | List all connections in the org |
get_connection | Get connection details by ID |
introspect_connection | List schemas/tables of a source connection |
preview_connection | Preview first N rows of a table |
query_connection | Execute a read-only SQL query |
compile_transformation | Compile a dbt transformation (no execution) |
preview_transformation | Compile + execute, return preview rows |
list_uploads | List all uploads |
list_pipelines | List all pipelines |
list_transformations | List all transformations |
list_runs | List runs with optional filters |
get_run | Get run details by ID |
get_run_logs | Get run logs |
list_catalog | List catalog entries (source tables + dbt models) |
get_catalog_entry | Get catalog entry details |
--allow-write| Tool | Description |
|---|---|
create_connection | Create a new data connection |
create_upload | Create a new upload (extract + load) |
create_pipeline | Create a new pipeline (dbt orchestration) |
create_transformation | Create a new dbt SQL transformation |
bulk_import | Bulk-create resources from JSON v2 format |
trigger_upload | Trigger an upload run |
trigger_pipeline | Trigger a pipeline run |
trigger_transformation | Trigger a transformation run |
Point --url at your instance. Use the backend port (8000) — 3000 is the Reflex frontend and serves no /api/v1:
datanika-mcp --url http://localhost:8000 --api-key etf_your_key
datanika-mcp publishes to PyPI via GitHub Actions Trusted Publishing (OIDC — no stored API token). To cut a release (from master):
version in pyproject.toml.pyproject.toml: git tag mcp-v0.2.0
git push origin mcp-v0.2.0
Release datanika-mcp to PyPI workflow builds the sdist + wheel and publishes. Verify: uvx datanika-mcp --help resolves from PyPI.First-release setup: a one-time PyPI trusted-publisher must be configured (project
datanika-mcp, repodatanika-io/datanika-core, workflowrelease-mcp.yml, environmentpypi) before the first tag will publish. See the infra human-locker.
AGPL-3.0 — same as the core Datanika platform.