Cloudera Data Visualization MCP Server

A Model Context Protocol server that exposes the full Cloudera Data Visualization (CDV) REST API to AI agents. This lets LLMs list, create, update, and delete CDV resources—groups, users, roles, segments, filter associations, workspaces, datasets, visuals, and data connections—as well as run jobs, query the data API, import/export migrations, and perform operational debugging tasks.

Tools

Groups

Tool	Description
list_groups	List all groups
get_group(object_id)	Get a group by ID
create_group(body)	Create a new group
update_group(object_id, body)	Update a group by ID
delete_group(object_id)	Delete a group by ID

Users

Tool	Description
list_users	List all users
get_user(object_id)	Get a user by ID
update_user(object_id, body)	Update a user by ID
edit_user_profile(username, body)	Edit a user's profile by username

Roles

Tool	Description
list_roles	List all roles
get_role(object_id)	Get a role by ID
create_role(body)	Create a new role
update_role(object_id, body)	Update a role by ID
delete_role(object_id)	Delete a role by ID

Segments

Tool	Description
list_segments	List all segments
get_segment(object_id)	Get a segment by ID
create_segment(body)	Create a new segment
update_segment(object_id, body)	Update a segment by ID
delete_segment(object_id)	Delete a segment by ID

Filter Associations

Tool	Description
list_filter_associations	List all filter associations
get_filter_association(object_id)	Get a filter association by ID
create_filter_association(body)	Create a new filter association
update_filter_association(object_id, body)	Update a filter association by ID
delete_filter_association(object_id)	Delete a filter association by ID

Workspaces

Tool	Description
list_workspaces	List all workspaces
get_workspace(object_id)	Get a workspace by ID
create_workspace(body)	Create a new workspace
update_workspace(object_id, body)	Update a workspace by ID
delete_workspace(object_id)	Delete a workspace by ID

Datasets

Tool	Description
list_datasets	List all datasets
get_dataset(object_id)	Get a dataset by ID
create_dataset(body)	Create a new dataset
update_dataset(object_id, body)	Update a dataset by ID
delete_dataset(object_id)	Delete a dataset by ID

Visuals

Tool	Description
list_visuals	List all visuals (charts/dashboards)
get_visual(object_id)	Get a visual by ID
create_visual(body)	Create a new visual (raw API body)
update_visual(object_id, body)	Update a visual by ID
delete_visual(object_id)	Delete a visual by ID
create_smart_visual(dataset_id, visual_type, title, columns, workspace_id?)	Create a chart visual — only exposes patterns confirmed to work via the API

Supported visual_type values for create_smart_visual:

Type	Use case	Notes
trellis-bars	Bar chart — one measure vs. one dimension	Measure on x, dimension on y
trellis-groupedbars	Grouped bars — one SUM measure, split by color dimension	Use "shelf": "color_shelf" for grouping
pie	Pie chart — SUM measure by dimension	Dimension auto-placed on color_shelf

All other CDV chart types require configuration in CDV's interactive builder and are blocked here to prevent broken visuals.

What is blocked and why:

Pattern	Error returned	Alternative
count aggregate	CDV generates count([col]) that Impala rejects	Use sum on a numeric column
trellis-lines, scatter, etc.	Unsupported visual type	Use CDV's builder; use query_dataapi for the data
Columns starting with avg_, sum_, etc. as measure targets	CDV tokenizer bug produces avg([avg_col])	Choose a different column
Date/timestamp columns as dimensions	CDV bracket conversion fails for TIMESTAMP	Use CDV's builder for time-series
filters parameter	WHERE ([col]) bracket SQL Impala rejects	Use query_dataapi for filtered queries; add filters in CDV's builder
Deleting dashboards	Cascade-deletes ALL linked chart visuals	Record visual IDs first; use delete_visual carefully

Connections

Tool	Description
list_connections	List all data connections
get_connection(object_id)	Get a connection by ID
create_connection(body)	Create a new connection
update_connection(object_id, body)	Update a connection by ID
delete_connection(object_id)	Delete a connection by ID
export_connection(object_id)	Export a connection definition by ID

Migrations

Tool	Description
export_migration	Export all CDV artifacts as a migration bundle
import_migration(body)	Import a CDV migration bundle

Data API

Tool	Description
query_dataapi_get(params)	Query /apps/dataapi via GET with optional parameters
query_dataapi_post(body)	Query /apps/dataapi via POST with a JSON body
query_data_get(params)	Query /api/data via GET with optional parameters
query_data_post(body)	Query /api/data via POST with a JSON body

Jobs

Tool	Description
run_job(body)	Trigger a job run
run_extract(body)	Run an existing extract job
create_extract(body)	Create a new extract job definition

Debugging / Operations

Tool	Description
get_gc_monitor	Retrieve GC monitor statistics
post_gc_monitor(body)	Trigger a GC monitor action
get_gc_stats	Retrieve GC statistics
post_gc_stats(body)	Trigger a GC stats action
get_log_levels	Get log levels for all loggers
set_log_level(body)	Set the default log level
get_logger_level(logger_name)	Get log level for a specific logger
set_logger_level(logger_name, body)	Set log level for a specific logger
get_toggle_cprofile	Get current cProfile tracing state
toggle_cprofile(body)	Toggle cProfile tracing on/off
reset_dataconnection_cache(connection_id)	Reset the cache for a data connection
reset_dataset_cache(dataset_id)	Reset the cache for a dataset

Examples

Cloudera Agent Studio — AI Assisted Supply Chain

The screenshots below show the CDV MCP Server being used inside Cloudera Agent Studio as part of an AI-powered supply chain workflow. The user asked a natural language question and the agent automatically:

1. Discovered the available connections and datasets
2. Explored the procurement_transactions table schema
3. Planned the right visualization type and column mapping
4. Called create_smart_visual to build a stacked bar chart in CDV
5. Called create_dashboard to make it visible in the Logistics MCP Demo workspace

Prompt: "Which suppliers are driving the most urgent spend? Build a stacked bar chart in Data viz."

Step 1 — Agent plans the visualization

The agent reasons through the dataset, identifies supplier_name as the dimension, priority_code as the color grouping, and total_price (sum) as the measure, then maps these to a trellis-groupedbars visual type.

Step 2 — Visual created and linked in CDV

The agent confirms the visualization details, creates the chart via create_smart_visual, wraps it in a dashboard with create_dashboard, and returns a direct link to the new artifact in the Logistics MCP Demo workspace.

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Agent Workflow Guidance

CDV Data Hierarchy

CDV organizes data in three layers, each building on the one below:

Connection  (links to an external database, e.g. Impala, Hive, Spark SQL)
  └── Dataset  (points to a specific table or query within that connection)
        └── Visual / Dashboard  (chart or dashboard built on a dataset)

Agents must respect this hierarchy when exploring or creating resources.

Discovery — Always Check Before Creating

Before creating anything, always discover what already exists at each level.

Step 1 — Connections

Call list_connections to see what data sources are registered in CDV.
Present the list to the user and identify which connection holds their data.
Only offer create_connection if no suitable connection exists and the user explicitly asks for one.

Step 2 — Datasets

Call list_datasets to see what datasets already exist (these are built on top of connections).
Present the results and ask the user which dataset to use.
Only offer create_dataset if no suitable dataset exists on the right connection and the user explicitly confirms they want a new one. Note that create_dataset requires a dc_id — the connection ID from Step 1.

Step 3 — Visuals / Dashboards

Call list_visuals to see what charts/dashboards already exist before building new ones.
Only call create_smart_visual or create_visual once the dataset has been confirmed.

Never assume anything needs to be created. CDV instances typically already have connections, datasets, and even visuals the user can reuse.

Correct flow:

User: "Build me a bar chart of shipment costs"
Agent: list_connections()            ← discover data sources
Agent: "I found these connections: ..."
Agent: list_datasets()               ← discover existing datasets
Agent: "Which dataset should I use?"
User: "Use supplier_shipping_performance"
Agent: list_workspaces()             ← identify target workspace
Agent: create_smart_visual(...)      ← now build the visual

Incorrect flow (avoid):

User: "Build me a bar chart of shipment costs"
Agent: create_connection(...)        ← ❌ never skip discovery
Agent: create_dataset(...)           ← ❌ never create without confirming first
Agent: create_smart_visual(...)

Workspace Discovery — Same Rule Applies

Always call list_workspaces before creating a visual or dashboard. Identify the correct workspace ID from the list; only call create_workspace if the user explicitly requests a new one.

Visual Creation Workflow

When create_smart_visual is used:

1. Call list_connections → understand available data sources.
2. Call list_datasets → confirm dataset with user (note which dc_id it belongs to).
3. Call list_workspaces → identify the target workspace ID.
4. Call create_smart_visual with dataset_id, workspace_id, and column specs.
   • If the Smart Visual API returns 404 (not available on all CDV instances), the tool automatically falls back to the standard admin API using the provided workspace_id.

Environment Variables

Variable	Required	Description
CDV_BASE_URL	Yes	Base URL of your CDV deployment (e.g. https://my-cdv.example.com)
CDV_API_KEY	Yes	CDV API key — sent as Authorization: apikey <key>
MCP_TRANSPORT	No	Transport protocol: stdio (default), http, or sse

Usage with Claude Desktop

Add the following to the mcpServers section of your claude_desktop_config.json:

Option 1: Direct installation from GitHub (Recommended)

{
  "mcpServers": {
    "cdv-mcp-server": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/kevintalbert/cdv-mcp-server@main",
        "run-server"
      ],
      "env": {
        "CDV_BASE_URL": "https://my-cdv-instance.example.com",
        "CDV_API_KEY": "your-api-key-here",
        "CDV_USERNAME": "vizapps_admin",
        "CDV_PASSWORD": "vizapps_admin"
      }
    }
  }
}

Option 2: Local installation (after cloning the repository)

{
  "mcpServers": {
    "cdv-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/CDV-MCP-Server",
        "run",
        "src/cdv_mcp_server/server.py"
      ],
      "env": {
        "CDV_BASE_URL": "https://my-cdv-instance.example.com",
        "CDV_API_KEY": "your-api-key-here",
        "CDV_USERNAME": "vizapps_admin",
        "CDV_PASSWORD": "vizapps_admin"
      }
    }
  }
}

For Option 2, replace /path/to/CDV-MCP-Server with your local path.

Local Development

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install dependencies
uv sync

# Run the server (stdio transport, default)
uv run run-server

# Run with HTTP transport
MCP_TRANSPORT=http uv run run-server

You can also create a .env file in the project root with your credentials:

CDV_BASE_URL=https://my-cdv-instance.example.com
CDV_API_KEY=your-api-key-here
CDV_USERNAME=vizapps_admin
CDV_PASSWORD=your-cdv-password

Transport

The MCP server's transport protocol is configurable via the MCP_TRANSPORT environment variable:

• stdio (default) — communicate over standard input/output. Useful for local tools, CLI scripts, and integrations like Claude Desktop.
• http — expose an HTTP server. Useful for web-based deployments and microservices.
• sse — use Server-Sent Events (SSE) transport. Useful for existing web-based deployments that rely on SSE.

Authentication

The server uses two separate authentication mechanisms with different capability levels:

Credential	Env Var	Required	Unlocks
CDV Admin API Key	CDV_API_KEY	Always	Data exploration, admin CRUD, connections, datasets, workspaces, users, groups, migrations, jobs
CDV Username + Password	CDV_USERNAME / CDV_PASSWORD	Optional	create_smart_visual, create_dashboard, create_visual, update_visual, delete_visual

Without CDV_USERNAME / CDV_PASSWORD

When session credentials are absent, chart and dashboard creation tools are not registered in the MCP server at all — they will not appear in the agent's tool list. The server operates in a read/explore-only mode for visuals:

• ✅ query_dataapi — run SQL queries, explore data
• ✅ list_connections, list_datasets, list_workspaces — discover resources
• ✅ list_visuals, get_visual — inspect existing dashboards
• ✅ All admin tools (users, groups, roles, datasets, etc.)
• ❌ create_smart_visual — not available
• ❌ create_dashboard — not available
• ❌ create_visual / update_visual / delete_visual — not available

Why two credentials?

CDV's admin API (arc/adminapi/v1/visuals) creates visual metadata (title, type, dataset) but does not persist shelf configurations (which columns appear on which axes). Shelf data is stored through CDV's UI API (arc/reports/report/{id}), which requires a browser-style session login. Without CDV_USERNAME/CDV_PASSWORD, visuals would be created as empty skeletons that render blank in CDV's frontend.

The session is cached in memory and reused across tool calls within a server process.

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