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spix_playbook_language_list

Retrieve the list of languages supported by Spix playbooks for voice calls, email, and contact management, enabling selection of appropriate language settings.

Instructions

List supported languages

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • CommandSchema definition for the 'playbook.language.list' command. This is the source-of-truth schema that defines the tool path, HTTP method (GET), API endpoint (/playbooks/languages), CLI usage, and that it is exposed as an MCP tool with 'safe' profile. It has no parameters (path params or query params), just returns the list of supported languages.
    CommandSchema(
        path="playbook.language.list",
        cli_usage="spix playbook language list",
        http_method="GET",
        api_endpoint="/playbooks/languages",
        mcp_expose="tool",
        mcp_profile="safe",
        description="List supported languages",
    ),
  • MCP tool registration: the schema at path 'playbook.language.list' (from COMMAND_REGISTRY) is converted to the MCP tool name 'spix_playbook_language_list' via the pattern f'spix_{{schema.path.replace(".", "_")}}'. The tool is registered with its description and inputSchema (built from build_json_schema).
    tool_schemas = get_mcp_tools(profile=tool_profile, disabled=disabled_tools)
    tool_defs: list[Tool] = []
    
    for schema in tool_schemas:
        # Convert path to tool name: playbook.create -> spix_playbook_create
        tool_name = f"spix_{schema.path.replace('.', '_')}"
        tool_defs.append(
            Tool(
                name=tool_name,
                description=schema.description or f"Spix {schema.path}",
                inputSchema=build_json_schema(schema),
            )
        )
    
    @server.list_tools()
    async def list_tools() -> list[Tool]:
        return tool_defs
  • The generic tool handler (create_tool_handler) that handles all tool calls including 'spix_playbook_language_list'. It resolves the tool name using get_schema_by_tool_name (which converts 'spix_playbook_language_list' -> 'playbook.language.list'), validates scope/access, builds the endpoint URL (/playbooks/languages), and dispatches a GET request to the backend API. Since playbook.language.list has no params, remaining_args will be empty.
    async def create_tool_handler(
        session: McpSessionContext,
        tool_name: str,
        arguments: dict,
    ) -> list:
        """Execute an MCP tool call by dispatching to the backend API.
    
        This function:
        1. Resolves the tool name to a command schema
        2. Validates session scope (playbook access, channel access)
        3. Builds the API request
        4. Dispatches to the backend
        5. Returns the response as MCP TextContent
    
        Args:
            session: The MCP session context for scope validation.
            tool_name: The MCP tool name (e.g., "spix_playbook_create").
            arguments: The tool arguments from the MCP client.
    
        Returns:
            List containing a single TextContent with the JSON response.
        """
        # Import here to avoid circular imports and handle missing mcp package
        try:
            from mcp.types import TextContent
        except ImportError:
            # Fallback for when mcp is not installed
            class TextContent:  # type: ignore[no-redef]
                def __init__(self, type: str, text: str) -> None:
                    self.type = type
                    self.text = text
    
        # Resolve tool name to schema
        schema = get_schema_by_tool_name(tool_name)
        if not schema:
            return [
                TextContent(
                    type="text",
                    text=orjson.dumps(
                        {"ok": False, "error": {"code": "unknown_tool", "message": f"Unknown tool: {tool_name}"}}
                    ).decode(),
                )
            ]
    
        # Validate tool access (not disabled)
        try:
            session.validate_tool_access(schema.path)
        except Exception as e:
            from spix_mcp.session import McpScopeError
    
            if isinstance(e, McpScopeError):
                return [TextContent(type="text", text=orjson.dumps({"ok": False, "error": e.to_dict()}).decode())]
            raise
    
        # Validate channel access if applicable
        channel = infer_channel_from_tool(schema.path)
        if channel:
            try:
                session.validate_channel_access(channel)
            except Exception as e:
                from spix_mcp.session import McpScopeError
    
                if isinstance(e, McpScopeError):
                    return [TextContent(type="text", text=orjson.dumps({"ok": False, "error": e.to_dict()}).decode())]
                raise
    
        # Handle playbook_id: validate and apply default
        playbook_id = arguments.get("playbook_id")
        try:
            effective_playbook = session.validate_playbook_access(playbook_id)
            if effective_playbook and not playbook_id:
                # Apply default playbook
                arguments["playbook_id"] = effective_playbook
        except Exception as e:
            from spix_mcp.session import McpScopeError
    
            if isinstance(e, McpScopeError):
                return [TextContent(type="text", text=orjson.dumps({"ok": False, "error": e.to_dict()}).decode())]
            raise
    
        # Build endpoint URL with path parameters
        endpoint, remaining_args = build_endpoint_url(schema, arguments)
    
        # Dispatch to backend API
        client = session.client
        method = schema.http_method.lower()
    
        if method == "get":
            response = await asyncio.to_thread(client.get, endpoint, params=remaining_args if remaining_args else None)
        elif method == "post":
            response = await asyncio.to_thread(client.post, endpoint, json=remaining_args if remaining_args else None)
        elif method == "patch":
            response = await asyncio.to_thread(client.patch, endpoint, json=remaining_args if remaining_args else None)
        elif method == "delete":
            response = await asyncio.to_thread(client.delete, endpoint, params=remaining_args if remaining_args else None)
        else:
            response = await asyncio.to_thread(client.get, endpoint)
    
        # Build response envelope
        envelope: dict = {"ok": response.ok, "meta": response.meta}
        if response.ok:
            envelope["data"] = response.data
            if response.pagination:
                envelope["pagination"] = response.pagination
            if response.warnings:
                envelope["warnings"] = response.warnings
        else:
            envelope["error"] = response.error
    
        return [TextContent(type="text", text=orjson.dumps(envelope).decode())]
Behavior3/5

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

No annotations are provided, so the description bears the full burden. It states a read-only operation ('list'), but does not disclose details such as the format of the output, whether the list is static or dynamic, or any side effects. The minimal description is not misleading but lacks depth. A score of 3 is appropriate given the tool's simplicity.

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 extremely concise—only three words—and conveys the essential purpose without any waste. Every word is necessary and earns its place. It is appropriately front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there is no output schema, the description should hint at the return format. 'List supported languages' does not specify whether the output is a list of strings, objects, or other types. While the tool is simple, this missing detail could lead to uncertainty. A score of 3 reflects adequate but incomplete context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, so description coverage is 100%. The description does not add parameter information, but none is needed. According to guidelines, a baseline of 4 applies when no parameters exist and description coverage is high.

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

Purpose5/5

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

The description 'List supported languages' clearly states the action (list) and the resource (supported languages). This distinguishes it from sibling tools like spix_playbook_clone or spix_playbook_list, which have different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

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. However, since it is a simple list with no parameters, the usage is implicitly understood—to retrieve the set of supported languages. A score of 3 reflects the lack of explicit context but adequate implied usage.

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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