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aliyun

Hologres MCP Server

Official
by aliyun

execute_hg_ddl_sql

Execute CREATE, ALTER, or DROP SQL statements to manage Hologres database objects like tables, views, and procedures.

Instructions

Execute (CREATE, ALTER, DROP) SQL statements to CREATE, ALTER, or DROP tables, views, procedures, GUCs etc. in Hologres databse.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe DDL SQL query to execute in Hologres database

Implementation Reference

  • Registration of the execute_hg_ddl_sql tool in list_tools(), including description and input schema.
    Tool(
        name="execute_hg_ddl_sql",
        description="Execute (CREATE, ALTER, DROP) SQL statements to CREATE, ALTER, or DROP tables, views, procedures, GUCs etc. in Hologres databse.",
        inputSchema={
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "The DDL SQL query to execute in Hologres database"
                }
            },
            "required": ["query"]
        }
    ),
  • Input schema definition for the execute_hg_ddl_sql tool: requires a 'query' parameter of type string.
    inputSchema={
        "type": "object",
        "properties": {
            "query": {
                "type": "string",
                "description": "The DDL SQL query to execute in Hologres database"
            }
        },
        "required": ["query"]
    }
  • Tool dispatch logic in call_tool(): validates the input query is a DDL statement.
    elif name == "execute_hg_ddl_sql":
        query = arguments.get("query")
        if not query:
            raise ValueError("Query is required")
        if not any(query.strip().upper().startswith(keyword) for keyword in ["CREATE", "ALTER", "DROP", "COMMENT ON"]):
            raise ValueError("Query must be a DDL statement (CREATE, ALTER, DROP, COMMENT ON)")
  • Core handler function that executes the SQL query on Hologres database via psycopg, handles results or success messages for DDL/DML.
    def handle_call_tool(tool_name, query, serverless = False):
        """Handle callTool method."""
        config = get_db_config()
        try:
            with connect_with_retry() as conn:
                with conn.cursor() as cursor:
    
                    # 特殊处理 serverless computing 查询
                    if serverless:
                        cursor.execute("set hg_computing_resource='serverless'")
                    
                    # Execute the query
                    cursor.execute(query)
                    
                    # 特殊处理 ANALYZE 命令
                    if tool_name == "gather_hg_table_statistics":
                        return f"Successfully {query}"
                    
                    # 处理其他有返回结果的查询
                    if cursor.description:  # SELECT query
                        columns = [desc[0] for desc in cursor.description]
                        rows = cursor.fetchall()
                        result = [",".join(map(str, row)) for row in rows]
                        return "\n".join([",".join(columns)] + result)
                    elif tool_name == "execute_dml_sql":  # Non-SELECT query
                        row_count = cursor.rowcount
                        return f"Query executed successfully. {row_count} rows affected."
                    else:
                        return "Query executed successfully"
        except Exception as e:
            return f"Error executing query: {str(e)}"
  • Helper function for reliable database connection with retries, used by handle_call_tool.
    def connect_with_retry(retries=3):
        attempt = 0
        err_msg = ""
        while attempt <= retries:
            try:
                config = get_db_config()
                conn = psycopg.connect(**config)
                conn.autocommit = True
                with conn.cursor() as cursor:
                    cursor.execute("SELECT 1;")
                    cursor.fetchone()
                return conn
            except psycopg.Error as e:
                err_msg = f"Connection failed: {e}"
                attempt += 1
                if attempt <= retries:
                    print(f"Retrying connection (attempt {attempt + 1} of {retries + 1})...")
                    time.sleep(5)  # 等待 2 秒后再次尝试连接
        raise psycopg.Error(f"Failed to connect to Hologres database after retrying: {err_msg}")
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it indicates this is for executing DDL statements (implying schema modifications), it lacks critical details such as required permissions, whether operations are reversible, potential side effects on dependent objects, or error handling. For a mutation tool with zero annotation coverage, this is a significant gap.

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 a single, efficient sentence that front-loads the key information ('Execute (CREATE, ALTER, DROP) SQL statements') and specifies the resource scope. There is no wasted verbiage, and every word contributes to understanding the tool's purpose and usage context.

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 a DDL execution tool with no annotations and no output schema, the description is insufficient. It lacks details on behavioral traits (e.g., permissions, side effects), return values, or error handling. While it covers the basic purpose, it does not provide enough context for safe and effective use in a production environment.

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?

Schema description coverage is 100%, with the single parameter 'query' documented as 'The DDL SQL query to execute in Hologres database'. The description adds minimal value beyond the schema by specifying the types of SQL statements (CREATE, ALTER, DROP) but does not provide additional syntax, format, or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

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?

The description clearly states the action ('Execute SQL statements') and the specific resource types affected ('tables, views, procedures, GUCs etc. in Hologres database'), with explicit verb+resource pairing. It distinguishes from siblings like execute_hg_dml_sql or execute_hg_select_sql by specifying DDL operations (CREATE, ALTER, DROP) rather than DML or SELECT queries.

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

Usage Guidelines4/5

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

The description provides clear context for when to use this tool: for executing DDL SQL statements (CREATE, ALTER, DROP) in Hologres. It implies alternatives by specifying DDL operations, distinguishing it from siblings like execute_hg_dml_sql or execute_hg_select_sql. However, it does not explicitly state when NOT to use it or name specific alternatives, keeping it at a 4.

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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