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tables_db_create_rows

Add new data entries to an existing Appwrite database table by specifying database ID, table ID, and row data in JSON format.

Instructions

Create new Rows. Before using this route, you should create a new table resource using either a server integration API or directly from your database console.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
database_idYesDatabase ID.
table_idYesTable ID. You can create a new table using the Database service [server integration](https://appwrite.io/docs/references/cloud/server-dart/tablesDB#createTable). Make sure to define columns before creating rows.
rowsYesArray of rows data as JSON objects.
transaction_idNoTransaction ID for staging the operation.

Implementation Reference

  • The generic tool handler that executes any registered tool, including 'tables_db_create_rows'. It retrieves the bound method from the TablesDB instance's 'create_rows' method and invokes 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)}")]
  • Registers the TablesDB service with name 'tables_db', triggering dynamic tool registration for all public methods prefixed with 'tables_db_', including 'tables_db_create_rows'.
    if args.tables_db:
        tools_manager.register_service(Service(TablesDB(client), "tables_db"))
  • Default registration of the TablesDB service as 'tables_db' if no other services are specified explicitly.
    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 the tool definition for 'tables_db_create_rows', including constructing the name 'tables_db_create_rows' from service_name + method_name and building JSON schema from type hints and docstrings.
    # 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
        }
    )
  • Imports the TablesDB class from Appwrite SDK, whose 'create_rows' method becomes the core handler logic for the tool 'tables_db_create_rows'.
    from appwrite.services.tables_db import TablesDB
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions a prerequisite (table creation) but doesn't disclose critical behavioral traits: whether this is a write operation (implied but not stated), what permissions are needed, whether it's idempotent, what happens on failure, or what the return format looks like. For a mutation tool with zero annotation coverage, this is insufficient.

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 provides important context without unnecessary elaboration. Every sentence earns its place, though the link could be considered slightly verbose.

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 4 parameters, no annotations, and no output schema, the description is incomplete. It mentions a prerequisite but doesn't cover behavioral aspects like error handling, authentication requirements, or return values. The agent lacks sufficient context to use this tool confidently.

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 four parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema. 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 ('Create new Rows') and specifies the resource (rows in a database table). It distinguishes from sibling tools like 'tables_db_create_table' by focusing on rows rather than tables or columns, but doesn't explicitly contrast with 'tables_db_create_row' (singular).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides important prerequisite guidance ('Before using this route, you should create a new table resource'), which helps the agent understand when this tool is appropriate. However, it doesn't explicitly mention when NOT to use it or contrast with alternatives like 'tables_db_create_row' (singular) or 'tables_db_upsert_rows'.

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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