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Appwrite MCP Server

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by appwrite

tables_db_create_index

Create database indexes to optimize query performance by specifying columns for faster data retrieval in Appwrite tables.

Instructions

Creates an index on the columns listed. Your index should include all the columns you will query in a single request.

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).
keyYesIndex Key.
typeYesIndex type.
columnsYesArray of columns to index. Maximum of 100 columns are allowed, each 32 characters long.
ordersNoArray of index orders. Maximum of 100 orders are allowed.
lengthsNoLength of index. Maximum of 100

Implementation Reference

  • Executes the logic for all tools, including 'tables_db_create_index', by calling the bound method of the Appwrite TablesDB.create_index with 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 tool definitions and input schemas for all public methods of the TablesDB service, creating the 'tables_db_create_index' tool from TablesDB.create_index method with schema from type hints and docstrings.
    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 the TablesDB Appwrite service as 'tables_db', enabling all its methods as prefixed MCP tools like 'tables_db_create_index'.
    if args.tables_db:
        tools_manager.register_service(Service(TablesDB(client), "tables_db"))
  • ToolManager.register_service populates the tools registry by calling service.list_tools(), including the 'tables_db_create_index' 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())
  • MCP server.list_tools() handler that returns all registered tools, including 'tables_db_create_index'.
    @server.list_tools()
    async def handle_list_tools() -> list[types.Tool]:
        return tools_manager.get_all_tools()
Behavior2/5

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

With no annotations provided, the description carries full burden but lacks critical behavioral details. It mentions a design guideline but doesn't disclose permissions needed, whether the operation is idempotent, potential impacts on performance, error conditions, or what happens if an index already exists. This leaves the agent guessing about side effects and constraints beyond the basic action.

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 brief and front-loaded with the core action, using two sentences that are directly relevant. However, the second sentence is more of a tip than essential guidance, slightly reducing efficiency. Overall, it's well-structured with minimal waste.

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 complexity of a 7-parameter mutation tool with no annotations and no output schema, the description is insufficient. It lacks details on behavioral traits, error handling, return values, and practical usage context, leaving significant gaps for an agent to operate effectively in a database management scenario.

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 fully documents all 7 parameters (e.g., 'database_id', 'columns'). The description adds no additional meaning about parameters beyond implying columns are central, which is already clear from the schema. This meets the baseline for high schema coverage but doesn't enhance understanding with examples or context.

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 ('Creates an index') and the target ('on the columns listed'), which is specific and actionable. It distinguishes from siblings like 'tables_db_delete_index' or 'tables_db_get_index' by focusing on creation, but doesn't explicitly differentiate from other creation tools (e.g., 'tables_db_create_table'), leaving room for slight ambiguity in scope.

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 minimal guidance with 'Your index should include all the columns you will query in a single request,' which hints at a design best practice but doesn't specify when to use this tool versus alternatives (e.g., when to create an index vs. rely on existing ones, or how it compares to other database operations). No explicit when/when-not or sibling alternatives are mentioned, offering little practical 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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