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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"}
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 but offers minimal information. 'Create' implies a write/mutation operation, but the description doesn't address permissions needed, whether this action is reversible, potential side effects, or error conditions. It lacks context about what 'creating a column' entails operationally beyond the basic action.

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 at just three words, with zero wasted language. It's front-loaded with the essential action and resource. While arguably too brief for a mutation tool with no annotations, it achieves maximum efficiency within its limited scope.

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 mutation tool with 8 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what happens after creation, potential constraints, or how this integrates with the broader database system. The agent must rely entirely on the input schema for operational details, leaving significant contextual gaps.

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 no parameter information beyond what's already in the schema (which has 100% coverage). It doesn't explain relationships between parameters (e.g., that 'default' cannot be set when 'required' is true), provide examples, or clarify the meaning of 'key' versus other identifiers. With complete schema documentation, the baseline score of 3 is appropriate.

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 'Create a string column' clearly states the verb ('Create') and resource ('string column'), making the purpose immediately understandable. It distinguishes itself from sibling tools like 'tables_db_create_boolean_column' by specifying the column type, though it doesn't explicitly mention the database/table context which is implied by the tool name.

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. It doesn't mention prerequisites (e.g., needing an existing database and table), compare it to other column-creation tools, or indicate when a string column is appropriate versus other data types like integer or boolean columns.

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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