DataLens is a business intelligence and data visualization system: it connects to databases, models the data into datasets, and renders charts and dashboards on top of them. It is offered as a managed service in Yandex Cloud, deployed on-premise, and run internally at Yandex.
This file orients and routes. It does not teach any one interface — pick the interface below and follow the skill that owns it.
Data flow
database → connection → source → dataset → chart → dashboard
- connection — credentials and driver for one database.
- source — a table or a SQL query exposed by that connection.
- dataset — the modelling layer: fields, calculated fields, joins, parameters, row-level security.
- chart — one visualization. Wizard charts sit on a dataset; QL charts skip the dataset and query a connection directly; editor charts are custom JavaScript.
- dashboard — tabs, widgets, selectors, and layout over charts.
Build left to right and reference by id. Deleting upstream breaks everything downstream.
Object IDs and URLs
DataLens URLs for objects with an explicit type can use either of these forms:
/<object_type>/<object_id>-<readable_slug>— pass only the object ID before the readable suffix to the API./<object_type>/<object_id>— when the readable suffix is absent, the entire final path segment is the object ID.
Dashboard links have an additional short form: https://datalens.ru/<dashboard_id>. When the URL
is identified as a dashboard link, the only path segment is the dashboard ID; there is no object
type segment. For example, https://datalens.ru/bgquq1234567 refers to dashboard ID
bgquq1234567.
For an explicitly typed object, https://datalens.ru/connections/r6wa2j1ww5cad-demo refers to the connection ID
r6wa2j1ww5cad; demo is a human-readable URL suffix. If the URL is
https://datalens.ru/connections/r6wa2j1ww5cad, use the same complete segment as the ID. The same
rule applies to connections, datasets, and charts. Do not apply the root-level short form to those
object types.
Treat object IDs as opaque values. Prefer the value from Copy ID or the API response's id
field when available. If a URL is ambiguous or does not match these forms, do not guess the ID.
Do not confuse an object ID with a display name, readable URL suffix, or a field UUID.
Object model
Where entries are filed — a separate axis from the flow above. Connections, datasets, charts, and dashboards are all entries, and entries live in a container tree. Two schemes exist, and which one is available differs per installation:
newer collection
├── collection … nested, arbitrarily deep
└── workbook
└── entries connection, dataset, chart, dashboard
older folder tree, path-addressed e.g. Users/someone/reports
└── entries held directly, no workbook in between
The workbook is the unit of grouping and permissions in the newer scheme; the folder path plays that role in the older one. Never assume which is available — an installation may offer one, the other, or both, so establish it the way the interface skill tells you rather than writing path-based logic against a workbook-only deployment.
Installations
Never assume which one is in play — hosts, auth, and the available connectors and chart types all differ. Establish it before writing anything that talks to an API.
| Installation | UI | API host | Auth |
|---|---|---|---|
| Yandex Cloud | datalens.ru |
api.datalens.tech |
yc CLI IAM token, plus an organization id |
| On-premise | ask the user | ask the user | deployment-specific; commonly an OAuth token in the environment |
| Yandex Team (internal) | datalens.yandex-team.ru |
api.datalens.yandex.net |
internal |
Yandex Cloud is the managed service. On-premise covers both the open-source self-host
(docker compose, UI on :8080) and the commercial enterprise build. Yandex Team is the
internal Yandex installation — see Installation-specific detail below before answering anything
specific to it.
Tool-specific skills detect the installation for you — the SDK, for instance, ships a preflight that reports it. Do not hand-roll detection.
Choosing an interface
| Want to | Use | Skill |
|---|---|---|
| Manage entities from Python — create, update, inspect, export, clone | Python SDK (datalens-sdk on PyPI) |
datalens-sdk |
| Let an agent call the DataLens API directly through tools | MCP server (@datalens-tech/mcp) |
— see its README |
| Anything else programmatic | the public HTTP API | — see below |
| Explore, click around, look at a rendered chart | the web UI | — human work, not agent work |
SDK vs MCP. The SDK is the default for anything scripted, repeatable, or committed to a
repository — it is typed, it has a documented object model, and the work survives as code. The MCP
server suits interactive one-offs inside an agent session: it fetches the API's OpenAPI spec at
startup and exposes a three-tool gateway (list_commands → describe_commands →
invoke_command) instead of hundreds of tools. Do not mix them for one task.
The HTTP API, when neither fits. The SDK and the MCP server are both built on it, so reach for
it directly only where they fall short. It is an HTTP API in an RPC style rather than a REST one:
you call named commands with JSON payloads, not resources. It is also self-describing — fetch the
OpenAPI spec from /json/ on the installation's API host (Installations above;
https://api.datalens.tech/json/ on the cloud) and work from the spec rather than guessing command
names or payload shapes. On the cloud the organization id is required, sent as an x-dl-org-id
header.
Installation-specific detail
Two installations withhold something you need, and in both cases the answer is to ask rather than to fill the gap yourself.
Yandex Team is extended by a skill distributed separately, datalens-yateam. If it is present
in the available skills list and the environment targets that installation, invoke it: it adds and
overrides installation-specific detail from this file, and it wins where the two disagree. If the
task concerns Yandex Team and the skill is not in the list, stop and ask the user to install it,
following the instructions at https://datalens.yandex-team.ru/get-skills. Do not reconstruct its
content in the meantime — say which part of the answer is installation-dependent and leave it
there.
On-premise publishes no endpoints, and the API does not necessarily sit on the UI's host. Stop and ask the user for both — the DataLens endpoint and the DataLens API endpoint — before writing anything that talks to either. Do not derive one from the other, do not fall back to a cloud default, and do not carry on with a placeholder.
Out of scope
State the boundary and stop rather than improvising an adjacent solution:
- What a metric means for the business, or whether a number looks right.
- Screenshotting or driving the web UI.
- Embedding DataLens in another product, or iframes.
- SQL and analysis that does not touch DataLens entities.