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Appwrite MCP Server

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tables_db_create_string_column

Add a text column to an Appwrite database table to store character-based data with configurable size, requirements, and security options.

Instructions

Create a string column.

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.
sizeYesColumn size for text columns, in number of characters.
requiredYesIs column required?
defaultNoDefault value for column when not provided. Cannot be set when column is required.
arrayNoIs column an array?
encryptNoToggle encryption for the column. Encryption enhances security by not storing any plain text values in the database. However, encrypted columns cannot be queried.

Implementation Reference

  • Universal handler for all tools. For 'tables_db_create_string_column', it retrieves the bound TablesDB.create_string_column method from the registry and invokes it with user arguments, handling results and errors.
    @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)}")]
  • Dynamically generates tool definitions for all public methods on TablesDB(client), including schema from type hints and docstrings. Constructs tool name as 'tables_db_create_string_column' from service_name='tables_db' and method_name='create_string_column'.
    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
  • Registers the TablesDB service (with service_name='tables_db') which triggers introspection and registration of its methods as tools, including 'tables_db_create_string_column'.
    def register_services(args):
        # If --all is specified, enable all services
        if args.all:
            args.tables_db = args.users = args.teams = args.storage = True
            args.functions = args.messaging = args.locale = args.avatars = True
            args.sites = True
    
        # Register services based on CLI arguments
        if args.tables_db:
            tools_manager.register_service(Service(TablesDB(client), "tables_db"))
        if args.users:
            tools_manager.register_service(Service(Users(client), "users"))
        if args.teams:
            tools_manager.register_service(Service(Teams(client), "teams"))
        if args.storage:
            tools_manager.register_service(Service(Storage(client), "storage"))
        if args.functions:
            tools_manager.register_service(Service(Functions(client), "functions"))
        if args.messaging:
            tools_manager.register_service(Service(Messaging(client), "messaging"))
        if args.locale:
            tools_manager.register_service(Service(Locale(client), "locale"))
        if args.avatars:
            tools_manager.register_service(Service(Avatars(client), "avatars"))
        if args.sites:
            tools_manager.register_service(Service(Sites(client), "sites"))
        if args.databases:
            tools_manager.register_service(Service(Databases(client), "databases"))
    
        # If no services were specified, enable tables_db by default
        if not any([args.databases, args.tables_db, args.users, args.teams, args.storage,
                    args.functions, args.messaging, args.locale, args.avatars, args.sites]):
            tools_manager.register_service(Service(TablesDB(client), "tables_db"))
  • ToolManager.register_service appends the service and updates the global tools_registry with the tools from TablesDB service, storing definition and function for 'tables_db_create_string_column'.
    def register_service(self, service: Service):
        """Register a new service and its tools"""
        self.services.append(service)
        self.tools_registry.update(service.list_tools())
  • Helper function used to convert Python type hints of TablesDB.create_string_column parameters to JSON Schema for the tool's inputSchema.
    def python_type_to_json_schema(self, py_type: Any) -> dict:
        """Converts Python type hints to JSON Schema types."""
        type_mapping = {
            str: "string",
            int: "integer",
            float: "number",
            bool: "boolean",
            list: "array",
            dict: "object"
        }
        
        # Handle basic types
        if py_type in type_mapping:
            return {"type": type_mapping[py_type]}
        
        # Handle Optional types (Union[type, None])
        if hasattr(py_type, "__origin__") and py_type.__origin__ is Union:
            args = getattr(py_type, "__args__", ())
            if len(args) == 2 and args[1] is type(None):
                schema = self.python_type_to_json_schema(args[0])
                return schema
        
        # Handle List, Dict, and other generic types
        if hasattr(py_type, "__origin__"):
            origin = py_type.__origin__
            args = getattr(py_type, "__args__", ())
            
            # Handle List[T]
            if origin is list or origin is List:
                if args:
                    item_schema = self.python_type_to_json_schema(args[0])
                    return {
                        "type": "array",
                        "items": item_schema
                    }
                return {"type": "array"}
            
            # Handle Dict[K, V]
            if origin is dict or origin is Dict:
                if len(args) >= 2:
                    value_schema = self.python_type_to_json_schema(args[1])
                    return {
                        "type": "object",
                        "additionalProperties": value_schema
                    }
                return {"type": "object"}
        
        # Default to string for unknown types
        return {"type": "string"}
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