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pab1it0

adx-mcp-server

get_table_details

Retrieve table details like row count and extent size from Azure Data Explorer databases to analyze data structure and performance.

Instructions

Retrieves table details including TotalRowCount, HotExtentSize

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The get_table_details tool handler, registered via @mcp.tool decorator. Executes the KQL command '.show table {table_name} details' to fetch table metadata such as TotalRowCount and HotExtentSize, formats the results, and handles errors.
    @mcp.tool(description="Retrieves table details including TotalRowCount, HotExtentSize")
    async def get_table_details(table_name: str) -> List[Dict[str, Any]]:
        """Get detailed statistics and metadata for a table."""
        logger.info("Getting table details", table_name=table_name, database=config.database)
    
        if not config.cluster_url or not config.database:
            logger.error("Missing ADX configuration")
            raise ValueError("Azure Data Explorer configuration is missing. Please set ADX_CLUSTER_URL and ADX_DATABASE environment variables.")
    
        try:
            client = get_kusto_client()
            query = f".show table {table_name} details"
            result_set = client.execute(config.database, query)
            results = format_query_results(result_set)
            logger.info("Table details retrieved successfully", table_name=table_name)
            return results
        except Exception as e:
            logger.error("Failed to get table details", table_name=table_name, error=str(e), exception_type=type(e).__name__)
            raise
  • Shared helper function used by get_table_details (and other tools) to format raw Kusto query results into a standardized list of dictionaries.
    def format_query_results(result_set) -> List[Dict[str, Any]]:
        """
        Format Kusto query results into a list of dictionaries.
    
        Args:
            result_set: Raw result set from KustoClient
    
        Returns:
            List of dictionaries with column names as keys
        """
        if not result_set or not result_set.primary_results:
            logger.debug("Empty or null result set received")
            return []
    
        try:
            primary_result = result_set.primary_results[0]
            columns = [col.column_name for col in primary_result.columns]
    
            formatted_results = []
            for row in primary_result.rows:
                record = {}
                for i, value in enumerate(row):
                    record[columns[i]] = value
                formatted_results.append(record)
    
            logger.debug("Query results formatted", row_count=len(formatted_results), columns=columns)
            return formatted_results
        except Exception as e:
            logger.error(
                "Error formatting query results",
                error=str(e),
                exception_type=type(e).__name__
            )
            raise
  • Shared helper function used by get_table_details (and other tools) to create and configure the Kusto client with Azure credentials.
    def get_kusto_client() -> KustoClient:
        """
        Create and configure a Kusto client with appropriate Azure credentials.
    
        Prioritizes WorkloadIdentityCredential when running in AKS with workload identity,
        falls back to DefaultAzureCredential for other authentication methods.
    
        Returns:
            KustoClient: Configured Kusto client instance
        """
        tenant_id = os.environ.get('AZURE_TENANT_ID')
        client_id = os.environ.get('AZURE_CLIENT_ID')
        token_file_path = os.environ.get('ADX_TOKEN_FILE_PATH', '/var/run/secrets/azure/tokens/azure-identity-token')
    
        if tenant_id and client_id:
            logger.info(
                "Using WorkloadIdentityCredential",
                client_id=client_id,
                tenant_id=tenant_id,
                token_file_path=token_file_path
            )
            try:
                credential = WorkloadIdentityCredential(
                    tenant_id=tenant_id,
                    client_id=client_id,
                    token_file_path=token_file_path
                )
            except Exception as e:
                logger.warning(
                    "Failed to initialize WorkloadIdentityCredential, falling back",
                    error=str(e),
                    exception_type=type(e).__name__
                )
                credential = DefaultAzureCredential()
        else:
            logger.info("Using DefaultAzureCredential (missing WorkloadIdentity credentials)")
            credential = DefaultAzureCredential()
    
        try:
            kcsb = KustoConnectionStringBuilder.with_azure_token_credential(
                connection_string=config.cluster_url,
                credential=credential
            )
            client = KustoClient(kcsb)
            logger.debug("Kusto client initialized successfully", cluster_url=config.cluster_url)
            return client
        except Exception as e:
            logger.error(
                "Failed to create Kusto client",
                error=str(e),
                exception_type=type(e).__name__,
                cluster_url=config.cluster_url
            )
            raise
  • The @mcp.tool decorator registers the get_table_details function as an MCP tool with its description used for schema/input validation.
    @mcp.tool(description="Retrieves table details including TotalRowCount, HotExtentSize")
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states 'Retrieves' (read operation) but doesn't disclose behavioral traits like permissions needed, rate limits, error conditions, or what happens if the table doesn't exist. The description is minimal and lacks operational context.

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?

Extremely concise single sentence with zero waste. Front-loaded with the core action ('Retrieves'), followed by specific details. Every word earns its place, though it may be overly terse.

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

Completeness3/5

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

Given 1 parameter, no annotations, but an output schema exists (so return values are documented elsewhere), the description is minimally complete for a simple lookup tool. However, it lacks context about the tool's role among siblings and operational details, making it adequate but with clear gaps.

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?

Schema description coverage is 0%, so the description must compensate. It doesn't mention the 'table_name' parameter at all, nor explain its format, constraints, or examples. The description adds no parameter semantics beyond what the bare schema provides.

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 'Retrieves' and the resource 'table details', specifying two concrete metrics (TotalRowCount, HotExtentSize). It distinguishes from siblings like get_table_schema (schema vs. details) and list_tables (listing vs. retrieving details), though it doesn't explicitly contrast them.

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?

No guidance on when to use this tool versus alternatives like get_table_schema or sample_table_data. The description implies it's for retrieving specific table metrics, but doesn't specify use cases, prerequisites, or exclusions.

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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