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Appwrite MCP Server

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tables_db_create_datetime_column

Add a datetime column to an Appwrite database table using ISO 8601 format. Specify column properties like required status, default value, and array configuration.

Instructions

Create a date time column according to the ISO 8601 standard.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
database_idYesDatabase ID.
table_idYesTable ID.
keyYesColumn Key.
requiredYesIs column required?
defaultNoDefault value for the column in [ISO 8601](https://www.iso.org/iso-8601-date-and-time-format.html) format. Cannot be set when column is required.
arrayNoIs column an array?

Implementation Reference

  • Generic handler for all tools, including 'tables_db_create_datetime_column'. Retrieves the bound method (TablesDB.create_datetime_column) from the registry and executes it with arguments, handling errors and formatting output.
    @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 the tool definition, including input schema from type hints, docstrings, and signature for each service method. For TablesDB.create_datetime_column, generates 'tables_db_create_datetime_column' tool with schema.
    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 (among others) with ToolManager, using service name 'tables_db'. This triggers dynamic tool registration for all TablesDB methods, prefixed with 'tables_db_', including 'tables_db_create_datetime_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 tools_registry with tools from service.list_tools(), effectively registering 'tables_db_create_datetime_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 method parameters to JSON schema for tool inputSchema. Used in list_tools for schema generation of 'tables_db_create_datetime_column'.
    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 full burden for behavioral disclosure but offers minimal information. It states the creation action but doesn't cover permissions needed, whether this is idempotent, error conditions, or what happens on success (e.g., column creation confirmation). For a mutation tool with zero annotation coverage, this is inadequate.

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 core action ('Create a date time column') and includes essential format guidance. There's no wasted wording, repetition, or unnecessary elaboration—every word serves a clear purpose.

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 tool's complexity (a mutation operation with 6 parameters), lack of annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, error handling, or behavioral nuances like the effect of 'array' or 'required' parameters. For a create-column tool in a database context, more completeness is needed.

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%, providing clear documentation for all 6 parameters. The description adds value by specifying the ISO 8601 standard, which clarifies the datetime format context beyond the schema's mention in the 'default' parameter. However, it doesn't elaborate on parameter interactions or usage examples, so baseline 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 clearly states the action ('Create') and resource ('a date time column') with specific format guidance ('according to the ISO 8601 standard'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this from sibling tools like 'tables_db_create_boolean_column' or 'tables_db_update_datetime_column' beyond the column type, which prevents a perfect score.

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 sibling tools like 'tables_db_update_datetime_column' for modifications, or indicate typical use cases. This leaves the agent without context for tool selection.

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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