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by appwrite

tables_db_update_datetime_column

Modify a datetime column's properties in Appwrite database tables, including requirements and default values, without affecting existing data.

Instructions

Update a date time 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 for column when not provided. Cannot be set when column is required.
new_keyNoNew Column Key.

Implementation Reference

  • Registers the TablesDB Appwrite service with name 'tables_db'. This service dynamically generates MCP tools for each public method, prefixed with 'tables_db_', so 'update_datetime_column' becomes 'tables_db_update_datetime_column'.
    if args.tables_db:
        tools_manager.register_service(Service(TablesDB(client), "tables_db"))
    if args.users:
  • Generic handler for all MCP tools. For 'tables_db_update_datetime_column', it retrieves the bound method from TablesDB.update_datetime_column and executes it with the provided arguments.
    @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 JSON schema for the tool's input parameters based on the Appwrite method's type hints, docstring, and signature. Used for all tools including this one.
    tool_definition = Tool(
        name=tool_name,
        description=f"{docstring.short_description or "No description available"}",
        inputSchema={
            "type": "object",
            "properties": properties,
            "required": required
        }
    )
  • ToolManager.register_service adds the tools from a service (including all tables_db tools) to the global registry used by list_tools and call_tool.
    def register_service(self, service: Service):
        """Register a new service and its tools"""
        self.services.append(service)
        self.tools_registry.update(service.list_tools())
  • Parses method parameters to build 'properties' and 'required' for the inputSchema of each tool.
    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)
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 adds one important behavioral detail about default values not affecting existing rows, which is valuable context beyond the input schema. However, it doesn't mention other critical behaviors like whether this operation is reversible, what permissions are required, or what happens on failure.

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. The first sentence states the core purpose, and the second adds a critical behavioral constraint. Every word serves a clear purpose with zero waste.

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 datetime column. The context is incomplete but not entirely inadequate.

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 six parameters thoroughly. The description adds no additional parameter information beyond what's in the schema descriptions. The baseline score of 3 reflects adequate parameter documentation through the schema alone.

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 date time column') and specifies the resource type ('date time column'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'tables_db_update_boolean_column' or 'tables_db_update_string_column', but the 'date time' qualifier provides implicit differentiation.

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 'tables_db_create_datetime_column' or other column update tools. It mentions a behavioral constraint about default values not updating existing rows, but this doesn't help the agent decide when this tool is appropriate versus other options.

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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