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Appwrite MCP Server

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tables_db_create_boolean_column

Add a boolean column to an Appwrite database table to store true/false values, with options for required fields, default values, and array configurations.

Instructions

Create a boolean column.

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).
keyYesColumn Key.
requiredYesIs column required?
defaultNoDefault value for column when not provided. Cannot be set when column is required.
arrayNoIs column an array?

Implementation Reference

  • Registers the TablesDB Appwrite service as 'tables_db', dynamically generating tools like 'tables_db_create_boolean_column' from its methods.
    tools_manager.register_service(Service(TablesDB(client), "tables_db"))
  • Generic tool handler that retrieves the tool info from registry and calls the underlying Appwrite service method (TablesDB.create_boolean_column) with arguments, returning 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)}")]
  • Dynamically generates tool definitions including name ('tables_db_create_boolean_column'), description from docstring, and inputSchema from method signature and type hints for each public method on the TablesDB instance.
    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
  • Imports the Appwrite TablesDB service class whose methods implement the tool logic, including create_boolean_column.
    from appwrite.services.tables_db import TablesDB
  • Default registration of tables_db service if no flags specified.
    tools_manager.register_service(Service(TablesDB(client), "tables_db"))
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 states 'Create' implying a write/mutation operation, but doesn't disclose behavioral traits like required permissions, whether it's idempotent, error conditions, or what happens on success/failure. This is inadequate for a tool that modifies database schema.

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 extremely concise ('Create a boolean column.') with zero wasted words, making it front-loaded and easy to parse. However, it's arguably too brief given the tool's complexity, bordering on under-specification rather than optimal conciseness.

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?

Given no annotations, no output schema, and a mutation tool with 6 parameters, the description is incomplete. It doesn't explain what the tool returns, error handling, or important behavioral aspects like side effects. For a database schema modification tool, this leaves significant gaps for an AI agent.

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 parameters are fully documented in the schema. The description adds no additional meaning about parameters beyond implying a boolean column is created. This meets the baseline of 3 where the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Create a boolean column' clearly states the action (create) and resource (boolean column), but it's vague about scope and doesn't differentiate from sibling tools like 'tables_db_create_string_column' or 'tables_db_create_float_column' beyond the column type. It lacks specificity about what system/database this operates on.

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 like other column-creation tools (e.g., for different data types) or general table operations. The description doesn't mention prerequisites, constraints, or typical use cases, leaving the agent without contextual usage cues.

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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