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tables_db_create_line_column

Add a geographic line column to store coordinate paths in Appwrite database tables for spatial data management.

Instructions

Create a geometric line column.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
database_idYesDatabase ID.
table_idYesTable ID. You can create a new table using the TablesDB 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, two-dimensional array of coordinate pairs, [[longitude, latitude], [longitude, latitude], …], listing the vertices of the line in order. Cannot be set when column is required.

Implementation Reference

  • Generic handler for all tools, including 'tables_db_create_line_column'. Retrieves the tool info from registry, binds the arguments, calls the underlying Appwrite SDK method (TablesDB.create_line_column), and formats 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 schema by introspecting methods on the TablesDB service instance. For 'tables_db_create_line_column', it uses the 'create_line_column' method's signature, docstring, and type hints.
    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 under the name 'tables_db', enabling dynamic tool generation for its methods, including 'tables_db_create_line_column'.
    if args.tables_db:
        tools_manager.register_service(Service(TablesDB(client), "tables_db"))
  • Default registration of the TablesDB service if no specific services are enabled.
    tools_manager.register_service(Service(TablesDB(client), "tables_db"))
  • Imports the TablesDB class from Appwrite SDK, which provides the underlying methods proxied as MCP tools.
    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 states 'Create' which implies a write/mutation operation, but doesn't disclose behavioral traits like required permissions, whether this is idempotent, error conditions, or what happens on success/failure. The description is minimal and lacks crucial context for safe invocation.

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 front-loaded with the core purpose and appropriately sized for what it conveys. Every word earns its place, making it efficient and easy to parse.

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 (creating a specialized geometric column in a database system), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the operation's behavior, return values, error handling, or relationship to other tools. For a mutation tool with no structured safety hints, 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 what's in the schema (e.g., it doesn't explain what a 'geometric line column' entails or provide usage examples). With high schema coverage, the baseline is 3 even without param info in the description.

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') and resource ('geometric line column'), which is specific and unambiguous. It distinguishes this tool from siblings like 'tables_db_create_point_column' or 'tables_db_create_polygon_column' by specifying the column type. However, it doesn't explicitly mention the database/table context, which is implied but could be more explicit.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing database and table), compare it to other column-creation tools (like 'tables_db_create_string_column'), or indicate when a line column is appropriate versus other geometric types. Usage is implied through the tool name but not explained.

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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