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spix_playbook_update

Modify playbook settings for AI agents, including goals, persona, briefing, and TTS parameters, to customize automated communication workflows.

Instructions

Update playbook configuration

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
playbook_idYesPlaybook ID
nameNoNew playbook name
goalNoNew goal
personaNoAI persona description
briefingNoPlaybook briefing text
contextNoNew context
voice_idNoVoice UUID for TTS
languageNoLanguage code
default_emotionNoDefault TTS emotion
speaking_rateNoSpeaking rate (0.6-1.5)
tts_volumeNoTTS volume (0.5-2.0)

Implementation Reference

  • Definition of the 'playbook.update' command in the registry, which generates the 'spix_playbook_update' MCP tool.
        path="playbook.update",
        cli_usage="spix playbook update <playbook_id> [--name <n>] [--goal <g>]",
        http_method="PATCH",
        api_endpoint="/playbooks/{playbook_id}",
        mcp_expose="tool",
        mcp_profile="safe",
        description="Update playbook configuration",
        positional_args=[
            CommandParam("playbook_id", "string", required=True, description="Playbook ID"),
        ],
        params=[
            CommandParam("name", "string", description="New playbook name"),
            CommandParam("goal", "string", description="New goal"),
            CommandParam("persona", "string", description="AI persona description"),
            CommandParam("briefing", "string", description="Playbook briefing text"),
            CommandParam("context", "string", description="New context"),
            CommandParam("voice_id", "uuid", description="Voice UUID for TTS"),
            CommandParam("language", "string", description="Language code"),
            CommandParam("default_emotion", "string", description="Default TTS emotion"),
            CommandParam("speaking_rate", "number", description="Speaking rate (0.6-1.5)"),
            CommandParam("tts_volume", "number", description="TTS volume (0.5-2.0)"),
        ],
    ),
  • The tool handler function that executes the logic for any generated MCP tool, including 'spix_playbook_update' which is mapped via `get_schema_by_tool_name`.
    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())]
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 'update' which implies mutation, but doesn't disclose behavioral traits like required permissions, whether changes are reversible, rate limits, or error conditions. The description adds minimal value beyond the basic action, leaving critical operational details unspecified.

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 zero waste. It's front-loaded with the core action and resource, making it immediately understandable without unnecessary elaboration.

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 (11 parameters, mutation operation) and lack of annotations or output schema, the description is incomplete. It doesn't address what constitutes a valid update, response format, error handling, or side effects. For a tool with significant configuration options, more context is needed to guide effective use.

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 11 parameters. The description adds no additional meaning beyond what the schema provides—it doesn't explain relationships between parameters, default behaviors, or constraints beyond those in schema descriptions. Baseline 3 is appropriate as the schema does the heavy lifting.

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 'Update playbook configuration' clearly states the action (update) and target resource (playbook configuration). It distinguishes from sibling tools like spix_playbook_create, spix_playbook_clone, and spix_playbook_show by specifying modification rather than creation, duplication, or retrieval. However, it doesn't explicitly mention what 'configuration' encompasses beyond the implied fields.

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 playbook ID), exclusions, or comparisons to siblings like spix_playbook_rule_add for more targeted updates. Usage is implied through the verb 'update' but lacks explicit context.

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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