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tables_db_update_boolean_column

Modify a boolean column in an Appwrite database table to change its required status, default value, or key identifier.

Instructions

Update a boolean column. Changing the default value will not update already existing rows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
database_idYesDatabase ID.
table_idYesTable ID. You can create a new table using the Database service [server integration](https://appwrite.io/docs/references/cloud/server-dart/tablesDB#createTable).
keyYesColumn Key.
requiredYesIs column required?
defaultYesDefault value for column when not provided. Cannot be set when column is required.
new_keyNoNew Column Key.

Implementation Reference

  • Registers the TablesDB service (tables_db) conditionally based on CLI flag. This service auto-generates MCP tools for all public methods on TablesDB(client), naming them 'tables_db_{method_name}', so 'tables_db_update_boolean_column' is registered if TablesDB has 'update_boolean_column' method.
    if args.tables_db:
        tools_manager.register_service(Service(TablesDB(client), "tables_db"))
    if args.users:
  • Default registration of TablesDB service (tables_db) if no other services specified. Enables the dynamic tool generation including 'tables_db_update_boolean_column'.
    tools_manager.register_service(Service(TablesDB(client), "tables_db"))
  • Dynamically lists all public methods on the service instance (e.g., TablesDB), overrides names if any, parses docstrings, generates JSON schema from type hints for parameters, creates MCP Tool objects, and maps tool_name to {'definition': Tool, 'function': bound_method}. This is where 'tables_db_update_boolean_column' tool is defined and schematized.
    def list_tools(self) -> Dict[str, Dict]:
        """Lists all available tools for this service"""
        tools = {}
    
        for name, func in inspect.getmembers(self.service, predicate=inspect.ismethod):
            if name.startswith('_'): # Skip private methods
                continue
    
            original_func = func.__func__
            
            # Skip if not from the service's module
            if original_func.__module__ != self.service.__class__.__module__:
                continue
    
            # Get the overridden name if it exists
            tool_name = self._method_name_overrides.get(name, f"{self.service_name}_{name}")
    
            docstring = parse(original_func.__doc__)
            signature = inspect.signature(original_func)
            type_hints = get_type_hints(original_func)
    
            properties = {}
            required = []
    
            for param_name, param in signature.parameters.items():
                if param_name == 'self':
                    continue
    
                param_type = type_hints.get(param_name, str)
                properties[param_name] = self.python_type_to_json_schema(param_type)
                properties[param_name]["description"] = f"Parameter '{param_name}'"
                
                for doc_param in docstring.params:
                    if doc_param.arg_name == param_name:
                        properties[param_name]["description"] = doc_param.description
    
                if param.default is param.empty:
                    required.append(param_name)
    
            tool_definition = Tool(
                name=tool_name,
                description=f"{docstring.short_description or "No description available"}",
                inputSchema={
                    "type": "object",
                    "properties": properties,
                    "required": required
                }
            )
            
            tools[tool_name] = {
                "definition": tool_definition,
                "function": func
            }
            
        return tools
  • Generic MCP tool execution handler. Retrieves the tool info from registry, gets the bound method (e.g., TablesDB.update_boolean_column), calls it with arguments, and returns the result as TextContent. This executes the 'tables_db_update_boolean_column' tool.
    @server.call_tool()
    async def handle_call_tool(
        name: str, arguments: dict | None
    ) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
        
        try:
            tool_info = tools_manager.get_tool(name)
            if not tool_info:
                raise McpError(f"Tool {name} not found")
            
            bound_method = tool_info["function"]
            result = bound_method(**(arguments or {}))
            if hasattr(result, 'to_dict'):
                result_dict = result.to_dict()
                return [types.TextContent(type="text", text=str(result_dict))]
            return [types.TextContent(type="text", text=str(result))]
        except AppwriteException as e:
            return [types.TextContent(type="text", text=f"Appwrite Error: {str(e)}")]
        except Exception as e:
            return [types.TextContent(type="text", text=f"Error: {str(e)}")]
  • ToolManager.register_service appends the service and updates the tools_registry with tools from service.list_tools(), making them available for get_tool and list_tools.
    def register_service(self, service: Service):
        """Register a new service and its tools"""
        self.services.append(service)
        self.tools_registry.update(service.list_tools())
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 adds one important constraint about default values not affecting existing rows, which is valuable. However, it doesn't address critical behavioral aspects like whether this is a mutating operation (implied but not stated), permission requirements, error conditions, or what happens when renaming columns via 'new_key'. For a tool that modifies database schema with 6 parameters, this is insufficient.

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 with just two sentences that each serve a clear purpose. The first sentence states the core functionality, and the second adds a critical behavioral constraint. There's zero wasted language or redundancy, making it efficiently front-loaded with essential information.

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?

For a schema modification tool with 6 parameters, no annotations, and no output schema, the description is inadequate. It lacks information about what the tool returns, error conditions, permission requirements, side effects of column renaming, or how it differs from general update operations. The single behavioral note about default values doesn't compensate for these significant gaps in a potentially destructive operation.

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%, so the schema already documents all 6 parameters thoroughly. The description adds minimal value beyond the schema - it only clarifies the behavioral implication of the 'default' parameter. No additional parameter semantics, constraints, or relationships are explained in the description beyond what's in the structured schema.

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 ('Update a boolean column') and specifies the resource type ('boolean column'), which distinguishes it from other column types in the sibling tools. However, it doesn't explicitly differentiate from other update operations like 'tables_db_update' or 'tables_db_update_row', leaving some ambiguity about when to use this specific tool versus general updates.

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. With many sibling tools including general 'tables_db_update' and other column-specific updates, there's no indication of prerequisites, constraints, or comparative context. The single behavioral note about default values doesn't address usage scenarios.

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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