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kevintalbert

Cloudera Data Visualization MCP Server

by kevintalbert

list_datasets

List all datasets available in Cloudera Data Visualization to retrieve their IDs and table names, enabling subsequent visual creation.

Instructions

STEP 2 of the workflow — list all datasets (named tables/views) available in CDV.

In CDV: Connection → Dataset → Visual → Dashboard A dataset is a named pointer to a specific table or SQL query within a connection. Visuals and dashboards are built on datasets (using their numeric dataset_id).

AFTER calling this tool, you will have dataset names and IDs, but NOT column names. ALWAYS follow up with: query_dataapi(dataconnection_id=<dc_id>, query="SELECT * FROM <schema.table> LIMIT 3") to discover the exact column names before creating any visual.

Workflow reminder:

  1. list_connections() → find dc_id (connection ID for SQL queries)

  2. list_datasets() → find dataset_id and table name (THIS TOOL)

  3. query_dataapi(...) → discover column names and sample data ← DO THIS NEXT

  4. list_workspaces() → find workspace_id for visual creation

  5. create_smart_visual(...)

  6. create_dashboard(...)

Only call create_dataset() if no suitable dataset exists AND the user explicitly confirms they want a new one. Always confirm with the user before creating anything.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses that the tool returns dataset names and IDs but not column names, and mandates a follow-up query_dataapi call to discover columns. No annotations exist, so the description carries full burden; while it covers important behaviors, it omits potential errors or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections: purpose, definition, workflow steps, and warnings. Front-loaded with the main action, and every sentence serves a purpose despite being verbose for the context. Efficiently packs necessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema (not shown but indicated), the description complements it by explaining what the output contains and lacks, and how it fits into the overall workflow. Sufficient for correct tool invocation and sequencing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 0 parameters, so schema coverage is 100%. Description adds value by explicitly confirming no parameters are needed, which is helpful for an agent to understand the tool's simplicity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states 'list all datasets (named tables/views) available in CDV', differentiating it from create_dataset and other sibling tools. The verb 'list' and resource 'datasets' are specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly positions as STEP 2 of a detailed workflow, with clear instructions on when to call it (after list_connections) and when not to (avoid create_dataset without user confirmation). Provides a full workflow reminder for context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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