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adx-mcp-server

execute_query

Execute Kusto Query Language (KQL) queries to retrieve and analyze data from Azure Data Explorer databases.

Instructions

Executes a Kusto Query Language (KQL) query against the configured Azure Data Explorer database and returns the results as a list of dictionaries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for the 'execute_query' MCP tool. Decorated with @mcp.tool for registration. Executes KQL queries on Azure Data Explorer using KustoClient, formats results, and handles errors.
    @mcp.tool(description="Executes a Kusto Query Language (KQL) query against the configured Azure Data Explorer database and returns the results as a list of dictionaries.")
    async def execute_query(query: str) -> List[Dict[str, Any]]:
        """Execute a KQL query against the configured ADX database."""
        logger.info("Executing KQL query", database=config.database, query_preview=query[:100])
    
        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()
            result_set = client.execute(config.database, query)
            results = format_query_results(result_set)
            logger.info("Query executed successfully", row_count=len(results))
            return results
        except Exception as e:
            logger.error(
                "Query execution failed",
                error=str(e),
                exception_type=type(e).__name__,
                database=config.database
            )
            raise
  • Helper function to format raw Kusto query results into a list of dictionaries with column names as keys. Called by execute_query.
    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
  • Helper function to create and configure a KustoClient with Azure credentials (WorkloadIdentity or Default). Used by execute_query to obtain the client.
    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
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. It states the action ('Executes') and return format ('returns the results as a list of dictionaries'), but doesn't mention important behavioral aspects like authentication requirements, rate limits, query timeout behavior, error handling, or whether this is a read-only versus write operation. For a query execution tool with zero annotation coverage, this leaves significant gaps in understanding operational constraints.

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 perfectly concise and well-structured in a single sentence that efficiently communicates the core functionality. Every element earns its place: the action (execute), language specificity (KQL), target (Azure Data Explorer database), and return format (list of dictionaries). There's zero redundancy or unnecessary elaboration.

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 the tool's complexity (query execution against a database), the absence of annotations, and the presence of an output schema (which handles return value documentation), the description is minimally adequate. It covers the basic purpose and return format but lacks important context about authentication, permissions, performance characteristics, and error scenarios that would be needed for robust agent usage. The output schema existence relieves the description from explaining return values, but other gaps remain.

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

Parameters3/5

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

The description adds minimal parameter semantics beyond the schema. It mentions 'Kusto Query Language (KQL) query' which provides context for the 'query' parameter, but with 0% schema description coverage and only one parameter, this is basic information. The description doesn't elaborate on query format requirements, size limitations, or parameter validation rules. Given the single parameter and low schema coverage, the description provides just enough context to meet baseline expectations.

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 action ('Executes a Kusto Query Language (KQL) query') and target resource ('against the configured Azure Data Explorer database'), with a specific verb+resource combination. It distinguishes from sibling tools like 'list_tables' or 'get_table_schema' by focusing on query execution rather than metadata retrieval. However, it doesn't explicitly differentiate from hypothetical similar query tools beyond the KQL specificity.

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. While it mentions executing KQL queries, it doesn't indicate when to prefer this over sibling tools like 'sample_table_data' for data exploration or whether there are prerequisites like database configuration. There's no explicit when/when-not usage context or named alternatives.

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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