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tables_db_update_polygon_column

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

Instructions

Update a polygon 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, three-dimensional array where the outer array holds one or more linear rings, [[[longitude, latitude], …], …], the first ring is the exterior boundary, any additional rings are interior holes, and each ring must start and end with the same coordinate pair. Cannot be set when column is required.
new_keyNoNew Column Key.

Implementation Reference

  • Registers the TablesDB Appwrite service instance with the tool manager under the name 'tables_db'. All public methods of TablesDB will be exposed as MCP tools prefixed with 'tables_db_', including 'tables_db_update_polygon_column'.
    tools_manager.register_service(Service(TablesDB(client), "tables_db"))
  • Generic MCP tool handler that executes any registered tool. For 'tables_db_update_polygon_column', it retrieves the bound method TablesDB.update_polygon_column and calls it with the input arguments, returning the result as text content.
    @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 of the service instance. For TablesDB.update_polygon_column, it creates the tool 'tables_db_update_polygon_column' with input schema derived from the method's type hints, signature, and docstring.
    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.register_service appends the service and merges its tools (from list_tools()) into the central registry, making them available via get_tool() and get_all_tools().
    def register_service(self, service: Service):
        """Register a new service and its tools"""
        self.services.append(service)
        self.tools_registry.update(service.list_tools())
  • MCP server list_tools handler that returns all tool definitions from the ToolManager, including the schema for 'tables_db_update_polygon_column'.
    @server.list_tools()
    async def handle_list_tools() -> list[types.Tool]:
        return tools_manager.get_all_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 discloses one important behavioral trait: 'Changing the `default` value will not update already existing rows.' This is valuable context about the mutation's scope. However, it doesn't mention permissions needed, whether the operation is reversible, error conditions, or what happens to the table structure.

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 just two sentences that both earn their place. The first sentence states the core purpose, and the second provides crucial behavioral context about default values. There's zero wasted text.

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 no annotations and no output schema, the description provides minimal but essential behavioral context about default values. However, it lacks information about what the tool returns, error conditions, or broader implications of updating a polygon column in a database table.

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 no additional parameter semantics 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 polygon column') and specifies the resource type, which distinguishes it from other column update tools for different data types. However, it doesn't explicitly differentiate from other polygon-related tools like 'tables_db_create_polygon_column' beyond the verb difference.

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 like creating a new column or using other update tools. It mentions a behavioral constraint about default values not affecting existing rows, but this doesn't help with tool selection decisions.

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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