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Appwrite MCP Server

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tables_db_create_url_column

Add a URL column to an Appwrite database table to store web addresses, with options for required fields, default values, and array storage.

Instructions

Create a URL column.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
database_idYesDatabase ID.
table_idYesTable ID.
keyYesColumn Key.
requiredYesIs column required?
defaultNoDefault value for column when not provided. Cannot be set when column is required.
arrayNoIs column an array?

Implementation Reference

  • Registers the TablesDB service from Appwrite under the 'tables_db' namespace. This enables all public methods of TablesDB (including create_url_column) to be exposed as MCP tools with names prefixed by 'tables_db_', e.g., 'tables_db_create_url_column'.
    if args.tables_db:
        tools_manager.register_service(Service(TablesDB(client), "tables_db"))
    if args.users:
  • The register_services function conditionally registers the TablesDB service based on CLI arguments or by default.
    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"))
  • Generic MCP tool execution handler. For 'tables_db_create_url_column', it retrieves the bound TablesDB.create_url_column method from the registry and executes it with the provided arguments.
    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 schema (inputSchema) from method type hints and docstrings, sets tool name as '{service_name}_{method_name}', and binds the handler function (service.method). This creates the 'tables_db_create_url_column' tool definition including its 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
  • Method to override tool names if needed. Returns empty dict, so default naming '{service_name}_{method_name}' is used for 'create_url_column'.
    def get_method_name_overrides(self) -> Dict[str, str]:
        """
        Override this method to provide method name mappings.
        Returns a dictionary where:
        - key: original method name
        - value: new method name to be used
        """
        return {}
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. 'Create a URL column' implies a mutation operation, but it doesn't specify permissions required, whether the creation is reversible, potential side effects, or what the response looks like. For a tool with 6 parameters and no annotations, this is a significant gap in transparency.

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 a single sentence 'Create a URL column.' It's front-loaded and wastes no words, though this brevity contributes to gaps in other dimensions. Every word serves a purpose, earning a high score for conciseness.

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 (6 parameters, mutation operation), lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects, usage context, or output expectations. For a tool that modifies database structure, more detail is needed to guide an AI agent effectively.

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 schema description coverage is 100%, so the schema already documents all 6 parameters with descriptions. The description adds no additional meaning beyond what the schema provides, such as explaining relationships between parameters (e.g., how 'default' interacts with 'required') or usage examples. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Create a URL column' states a clear verb ('Create') and resource ('URL column'), but it's vague about the scope and doesn't distinguish from sibling tools like 'tables_db_create_string_column' or 'tables_db_create_email_column'. It doesn't specify this is for database tables or mention the context 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?

No guidance is provided on when to use this tool versus alternatives like other column creation tools (e.g., 'tables_db_create_string_column'). It doesn't mention prerequisites, such as needing an existing database and table, or exclusions like when not to use it. The description offers no contextual usage information.

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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