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Appwrite MCP Server

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tables_db_list_indexes

Retrieve all database table indexes to analyze structure, optimize queries, and manage data organization in Appwrite projects.

Instructions

List indexes on the table.

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).
queriesNoArray of query strings generated using the Query class provided by the SDK. [Learn more about queries](https://appwrite.io/docs/queries). Maximum of 100 queries are allowed, each 4096 characters long. You may filter on the following columns: key, type, status, attributes, error

Implementation Reference

  • Registers the TablesDB service with service_name='tables_db', which dynamically generates and registers tools including 'tables_db_list_indexes' from TablesDB methods.
    if args.tables_db:
        tools_manager.register_service(Service(TablesDB(client), "tables_db"))
  • Default registration of TablesDB service if no other services specified.
    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"))
  • Generic MCP tool execution handler. For 'tables_db_list_indexes', retrieves the bound TablesDB.list_indexes method and executes it with provided arguments, formats result as TextContent.
    @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 (name, schema, description) by introspecting methods on the service instance (e.g., TablesDB.list_indexes becomes 'tables_db_list_indexes' with schema from type hints and docstring).
    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
  • Helper function that converts Python type hints of service methods to JSON schema for tool input validation.
    def python_type_to_json_schema(self, py_type: Any) -> dict:
        """Converts Python type hints to JSON Schema types."""
        type_mapping = {
            str: "string",
            int: "integer",
            float: "number",
            bool: "boolean",
            list: "array",
            dict: "object"
        }
        
        # Handle basic types
        if py_type in type_mapping:
            return {"type": type_mapping[py_type]}
        
        # Handle Optional types (Union[type, None])
        if hasattr(py_type, "__origin__") and py_type.__origin__ is Union:
            args = getattr(py_type, "__args__", ())
            if len(args) == 2 and args[1] is type(None):
                schema = self.python_type_to_json_schema(args[0])
                return schema
        
        # Handle List, Dict, and other generic types
        if hasattr(py_type, "__origin__"):
            origin = py_type.__origin__
            args = getattr(py_type, "__args__", ())
            
            # Handle List[T]
            if origin is list or origin is List:
                if args:
                    item_schema = self.python_type_to_json_schema(args[0])
                    return {
                        "type": "array",
                        "items": item_schema
                    }
                return {"type": "array"}
            
            # Handle Dict[K, V]
            if origin is dict or origin is Dict:
                if len(args) >= 2:
                    value_schema = self.python_type_to_json_schema(args[1])
                    return {
                        "type": "object",
                        "additionalProperties": value_schema
                    }
                return {"type": "object"}
        
        # Default to string for unknown types
        return {"type": "string"}
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 offers minimal behavioral context. It doesn't mention whether this is a read-only operation, what permissions are required, how results are paginated, or what format the output takes. 'List' implies read access, but lacks details on rate limits, error conditions, or system impact.

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, clear sentence with zero wasted words. It's perfectly front-loaded and appropriately sized for a straightforward list operation, earning full marks for efficiency.

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 tool with 3 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the output looks like (e.g., array of index objects with properties), behavioral constraints, or how to interpret the 'queries' parameter effectively. More context is needed for proper agent usage.

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 three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter documentation.

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 ('List') and target resource ('indexes on the table'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'tables_db_get_index' or 'tables_db_list_columns', which would require more specificity about scope or output format.

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 about when to use this tool versus alternatives like 'tables_db_get_index' (for a single index) or 'tables_db_list_columns' (for columns). The description only states what it does, not when it's appropriate or what prerequisites exist.

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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