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Databricks MCP Server

by samhavens

get_cluster

Retrieve detailed information about a specific Databricks cluster by providing its cluster ID to monitor configuration, status, and performance.

Instructions

Get information about a specific Databricks cluster

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cluster_idYes

Implementation Reference

  • MCP tool handler for 'get_cluster'. Decorated with @mcp.tool() to register it. Fetches cluster details using the clusters API and returns JSON response or error.
    @mcp.tool()
    async def get_cluster(cluster_id: str) -> str:
        """Get information about a specific Databricks cluster"""
        logger.info(f"Getting cluster info: {cluster_id}")
        try:
            result = await clusters.get_cluster(cluster_id)
            return json.dumps(result)
        except Exception as e:
            logger.error(f"Error getting cluster info: {str(e)}")
            return json.dumps({"error": str(e)})
  • Helper function in clusters API module that makes the actual Databricks API call to retrieve cluster information by ID.
    async def get_cluster(cluster_id: str) -> Dict[str, Any]:
        """
        Get information about a specific cluster.
        
        Args:
            cluster_id: ID of the cluster
            
        Returns:
            Response containing cluster information
            
        Raises:
            DatabricksAPIError: If the API request fails
        """
        logger.info(f"Getting information for cluster: {cluster_id}")
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Get information' implies a read-only operation, it doesn't specify what type of information is returned (configuration, status, metrics), whether authentication is required, rate limits, or error conditions. The description is too vague about the actual behavior beyond the basic operation.

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?

The description is extremely concise - a single sentence that directly states the tool's purpose. There's zero wasted language, and it's front-loaded with the essential information. Every word earns its place in this minimal description.

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

Completeness2/5

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

Given the complexity of cluster operations in Databricks, no annotations, no output schema, and 0% schema description coverage, this description is insufficient. It doesn't explain what information is returned, how to interpret results, or provide context about cluster states. For a tool that presumably returns detailed cluster information, the description is too minimal.

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

Parameters2/5

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

With 0% schema description coverage and 1 parameter, the description doesn't add any meaning beyond what the input schema provides. It mentions 'a specific Databricks cluster' which implies the cluster_id parameter, but doesn't explain where to obtain this ID, its format, or validation requirements. The description doesn't compensate for the complete lack of schema documentation.

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

Purpose4/5

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

The description clearly states the verb ('Get information') and resource ('about a specific Databricks cluster'), making the purpose evident. However, it doesn't distinguish this tool from its sibling 'list_clusters' which also provides cluster information but in a list format rather than for a specific cluster.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention that 'list_clusters' should be used to find cluster IDs first, or when to use this versus checking cluster status through other means. There's no explicit when/when-not usage context provided.

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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