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tables_db_update_point_column

Modify a point column in an Appwrite database table to adjust requirements, default values, or rename the column for geographic data storage.

Instructions

Update a point column. Changing the default value will not update already existing rows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
database_idYesDatabase ID.
table_idYesTable ID. You can create a new table using the TablesDB service [server integration](https://appwrite.io/docs/references/cloud/server-dart/tablesDB#createTable).
keyYesColumn Key.
requiredYesIs column required?
defaultNoDefault value for column when not provided, array of two numbers [longitude, latitude], representing a single coordinate. Cannot be set when column is required.
new_keyNoNew Column Key.

Implementation Reference

  • Registration of the TablesDB service under the name 'tables_db', which dynamically provides tools like 'tables_db_update_point_column' for each method on TablesDB.
    if args.tables_db:
        tools_manager.register_service(Service(TablesDB(client), "tables_db"))
  • Default registration of the TablesDB 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"))
  • Generic MCP tool handler that dispatches calls to the underlying Appwrite service method (TablesDB.update_point_column for this tool).
    @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 (including schema) for all public methods on the Appwrite service instance, naming them as '{service_name}_{method_name}' e.g. 'tables_db_update_point_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
  • ToolManager method that registers a service by adding its dynamically generated tools to the registry.
    def register_service(self, service: Service):
        """Register a new service and its tools"""
        self.services.append(service)
        self.tools_registry.update(service.list_tools())
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It adds one important behavioral note about default values not updating existing rows, which is valuable context beyond the basic 'update' action. However, it doesn't disclose other important behaviors like permissions needed, error conditions, or what the tool returns.

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 concise with two sentences. The first states the core purpose, and the second adds important behavioral context. There's no wasted language, though it could potentially benefit from more structure for complex tools.

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 no annotations and no output schema, the description is insufficient. It lacks information about what the tool returns, error conditions, permissions required, and how it differs from other column update tools. The single behavioral note about default values is helpful but doesn't compensate for the broader 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?

Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. The baseline of 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.

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 a point column') and specifies the resource type ('point column'), which is a specific column type. It distinguishes from general update tools by focusing on point columns, but doesn't explicitly differentiate from other column update siblings like 'tables_db_update_boolean_column' beyond the column type.

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. The description doesn't mention prerequisites, when this tool is appropriate compared to other column update tools, or any constraints beyond the behavioral note about default values.

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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