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tables_db_update_ip_column

Modify IP address column settings in Appwrite database tables, including requirements, default values, and column keys.

Instructions

Update an ip column. Changing the default value will not update already existing rows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
database_idYesDatabase ID.
table_idYesTable ID.
keyYesColumn Key.
requiredYesIs column required?
defaultYesDefault value. Cannot be set when column is required.
new_keyNoNew Column Key.

Implementation Reference

  • Generic handler for all MCP tools, including 'tables_db_update_ip_column'. Retrieves the tool info, binds the Appwrite TablesDB.update_ip_column method, calls it with arguments, and returns the result.
    @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)}")]
  • Registers the 'tables_db' service wrapping Appwrite TablesDB(client). This service's methods, including 'update_ip_column', are exposed as tools prefixed with 'tables_db_'.
    if args.tables_db:
        tools_manager.register_service(Service(TablesDB(client), "tables_db"))
  • Default registration of 'tables_db' service if no other services specified.
    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"))
  • Dynamically generates tool definitions (including schema) for all public methods of the TablesDB service, naming them 'tables_db_<method>' such as 'tables_db_update_ip_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
  • Method to override tool names; returns empty dict by default, so uses 'tables_db_<method_name>' naming.
    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 {}
Behavior3/5

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. It reveals one important behavioral constraint ('Changing the `default` value will not update already existing rows'), which is valuable context for a mutation operation. However, it doesn't address other critical behavioral aspects like permissions needed, whether the operation is reversible, error conditions, or what happens to data when changing column properties.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately brief (two sentences) and front-loaded with the core purpose. The second sentence adds important behavioral context. There's no wasted verbiage, though the structure could be slightly improved by explicitly mentioning it's for modifying IP column properties.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with 6 parameters and no annotations or output schema, the description is minimally adequate. It covers the basic purpose and one important behavioral constraint, but lacks context about permissions, error handling, return values, and how this tool fits within the broader column management workflow alongside its many siblings.

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 mentions the 'default' parameter behavior. This meets the baseline expectation when schema coverage is high, but doesn't provide additional semantic context about parameter interactions or usage patterns.

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 an ip column') and specifies the resource type. It distinguishes from general 'update' tools by focusing on IP columns, but doesn't explicitly differentiate from other column update tools like 'tables_db_update_boolean_column' or 'tables_db_update_string_column' in the sibling list.

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 for creating and updating different column types, there's no indication of when this specific IP column update tool is appropriate versus using a general update tool or creating a new column.

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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