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wenjiachengy

MySQL MCP Server

by wenjiachengy

execute_sql

Execute SQL queries on MySQL databases to retrieve, modify, or analyze data through secure database interactions.

Instructions

Execute an SQL query on the MySQL server

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe SQL query to execute

Implementation Reference

  • The @app.call_tool() handler function that implements the logic for the 'execute_sql' tool, including SQL execution, result handling, and error management.
    @app.call_tool()
    async def call_tool(name: str, arguments: dict) -> list[TextContent]:
        """Execute SQL commands."""
        config = get_db_config()
        logger.info(f"Calling tool: {name} with arguments: {arguments}")
        
        if name != "execute_sql":
            raise ValueError(f"Unknown tool: {name}")
        
        query = arguments.get("query")
        if not query:
            raise ValueError("Query is required")
        
        try:
            with connect(**config) as conn:
                with conn.cursor() as cursor:
                    cursor.execute(query)
                    
                    # Special handling for SHOW TABLES
                    if query.strip().upper().startswith("SHOW TABLES"):
                        tables = cursor.fetchall()
                        result = ["Tables_in_" + config["database"]]  # Header
                        result.extend([table[0] for table in tables])
                        return [TextContent(type="text", text="\n".join(result))]
                    
                    # Handle all other queries that return result sets (SELECT, SHOW, DESCRIBE etc.)
                    elif cursor.description:
                        columns = [desc[0] for desc in cursor.description]
                        rows = cursor.fetchall()
                        result = [",".join(map(str, row)) for row in rows]
                        return [TextContent(type="text", text="\n".join([",".join(columns)] + result))]
                    
                    # Non-SELECT queries
                    else:
                        conn.commit()
                        return [TextContent(type="text", text=f"Query executed successfully. Rows affected: {cursor.rowcount}")]
                    
        except Error as e:
            logger.error(f"Error executing SQL '{query}': {e}")
            return [TextContent(type="text", text=f"Error executing query: {str(e)}")]
  • The input schema definition for the 'execute_sql' tool, specifying the required 'query' parameter.
    inputSchema={
        "type": "object",
        "properties": {
            "query": {
                "type": "string",
                "description": "The SQL query to execute"
            }
        },
        "required": ["query"]
    }
  • The registration of the 'execute_sql' tool in the list_tools() function, including name, description, and schema.
    return [
        Tool(
            name="execute_sql",
            description="Execute an SQL query on the MySQL server",
            inputSchema={
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "The SQL query to execute"
                    }
                },
                "required": ["query"]
            }
        )
    ]
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions execution but lacks details on permissions needed, whether it's read-only or mutative, potential side effects, error handling, or response format. This leaves significant gaps in understanding the tool's behavior.

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, clear sentence with no wasted words, making it highly concise and front-loaded. It efficiently communicates the core purpose without unnecessary elaboration.

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 SQL execution (potentially mutative, with security and error implications), no annotations, and no output schema, the description is incomplete. It fails to address critical aspects like return values, safety warnings, or usage constraints, leaving the agent under-informed.

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 input schema has 100% description coverage, with the 'query' parameter documented as 'The SQL query to execute'. The description adds no additional meaning beyond this, such as query syntax examples or constraints. Baseline 3 is appropriate since the schema does the heavy lifting.

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 ('Execute') and target resource ('SQL query on the MySQL server'), making the purpose immediately understandable. It doesn't need to distinguish from siblings since there are none, but it could be more specific about what types of queries are supported (e.g., SELECT, INSERT, etc.).

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, prerequisites, or limitations. It simply states what the tool does without any context about appropriate use cases, making it minimally helpful for decision-making.

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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