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Appwrite MCP Server

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by appwrite

tables_db_create_operations

Execute multiple database operations within a single transaction to maintain data consistency and atomicity in Appwrite projects.

Instructions

Create multiple operations in a single transaction.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
transaction_idYesTransaction ID.
operationsNoArray of staged operations.

Implementation Reference

  • Registers the TablesDB Appwrite service with the name 'tables_db'. This service dynamically generates MCP tools for each public method on TablesDB, naming them 'tables_db_{method_name}', including 'tables_db_create_operations'.
    tools_manager.register_service(Service(TablesDB(client), "tables_db"))
  • Generic handler for executing all registered tools, including 'tables_db_create_operations'. Retrieves the bound SDK method from the registry and invokes it with user-provided arguments, handling errors and formatting results.
    @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 inspects service methods to generate Tool definitions with inputSchema based on type hints, docstrings, and signatures. For 'tables_db_create_operations', this produces the JSON schema from TablesDB.create_operations method inspection.
    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
  • Registers a service by adding its dynamically generated tools to the central registry, used when registering the 'tables_db' service.
    def register_service(self, service: Service):
        """Register a new service and its tools"""
        self.services.append(service)
        self.tools_registry.update(service.list_tools())
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions 'single transaction,' hinting at atomicity, but doesn't disclose critical behavioral traits such as whether this is a write operation, permission requirements, error handling, rollback behavior, or rate limits. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words. It's front-loaded with the core action and context, making it easy to parse quickly, though it could benefit from more detail given the tool's complexity.

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 the tool's complexity (a mutation tool for batch operations), lack of annotations, no output schema, and incomplete behavioral disclosure, the description is inadequate. It doesn't explain what 'operations' are, how they're structured, or what happens on success/failure, leaving 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 the schema already documents both parameters ('transaction_id' and 'operations'). The description adds minimal value by implying the parameters are used for creating operations in a transaction, but it doesn't provide additional semantics like format details or examples beyond what the schema states.

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 states the tool 'Create multiple operations in a single transaction,' which provides a verb ('Create') and resource ('operations'), but it's vague about what 'operations' entail and doesn't distinguish it from siblings like 'tables_db_create_transaction' or 'tables_db_create_rows.' It specifies 'multiple' and 'single transaction,' which adds some context but lacks specificity compared to other tools.

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 explicit guidance on when to use this tool versus alternatives is provided. The description implies usage for batch operations within a transaction, but it doesn't mention prerequisites, exclusions, or compare it to siblings like 'tables_db_create_rows' or 'tables_db_create_transaction,' leaving the agent without clear direction.

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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