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spix_contact_summary

Generate an AI-powered summary of a contact by providing their contact ID, enabling quick understanding of contact details without manual review.

Instructions

Get AI-generated contact summary

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contact_idYesContact ID

Implementation Reference

  • CommandSchema definition for contact.summary, which maps to the MCP tool name 'spix_contact_summary'. It defines a GET request to /contacts/{contact_id}/summary with a required contact_id positional arg.
    CommandSchema(
        path="contact.summary",
        cli_usage="spix contact summary <contact_id>",
        http_method="GET",
        api_endpoint="/contacts/{contact_id}/summary",
        mcp_expose="tool",
        mcp_profile="safe",
        description="Get AI-generated contact summary",
        positional_args=[
            CommandParam("contact_id", "string", required=True, description="Contact ID"),
        ],
    ),
  • Registration: Tools are dynamically generated from schemas in COMMAND_REGISTRY. The path 'contact.summary' becomes tool name 'spix_contact_summary' via the pattern f'spix_{schema.path.replace(".", "_")}'. The tool is then listed via list_tools() and dispatched via call_tool().
    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),
            )
        )
  • Generic handler function create_tool_handler() that dispatches ALL MCP tool calls. For 'spix_contact_summary', it resolves the schema, builds endpoint URL /contacts/{contact_id}/summary (substituting contact_id), and makes a GET request to the backend API.
    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())]
  • Helper function get_schema_by_tool_name() that resolves the MCP tool name 'spix_contact_summary' to its CommandSchema by converting 'contact_summary' -> 'contact.summary' and matching against COMMAND_REGISTRY.
    def get_schema_by_tool_name(tool_name: str) -> CommandSchema | None:
        """Look up a CommandSchema by MCP tool name.
    
        MCP tool names follow the pattern: spix_{path with dots replaced by underscores}
        e.g., "spix_playbook_create" -> "playbook.create"
    
        Args:
            tool_name: The MCP tool name (e.g., "spix_playbook_create").
    
        Returns:
            The matching CommandSchema, or None if not found.
        """
        # Remove the spix_ prefix
        if not tool_name.startswith("spix_"):
            return None
    
        path_part = tool_name[len("spix_") :]
    
        # Convert underscores back to dots for path lookup
        # We need to handle multi-part paths like "billing_credits_history" -> "billing.credits.history"
        # Try different dot positions to find the right one
        for cmd in COMMAND_REGISTRY:
            # Convert the command path to expected tool name format
            expected_tool = cmd.path.replace(".", "_")
            if expected_tool == path_part:
                return cmd
    
        return None
  • Helper function build_endpoint_url() that substitutes the contact_id positional arg into the endpoint URL /contacts/{contact_id}/summary.
    def build_endpoint_url(schema: CommandSchema, arguments: dict) -> tuple[str, dict]:
        """Build the API endpoint URL with path parameters substituted.
    
        Args:
            schema: The command schema.
            arguments: The tool arguments.
    
        Returns:
            Tuple of (endpoint_url, remaining_arguments).
            Path parameters are removed from arguments and substituted into the URL.
        """
        endpoint = schema.api_endpoint
        remaining_args = dict(arguments)
    
        # Substitute path parameters
        for param in schema.positional_args:
            placeholder = f"{{{param.name}}}"
            if placeholder in endpoint and param.name in remaining_args:
                endpoint = endpoint.replace(placeholder, str(remaining_args.pop(param.name)))
    
        return endpoint, remaining_args
Behavior2/5

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

With no annotations, the description should disclose behavioral traits. It only states the purpose, not whether the operation is read-only, requires permissions, or has limits. Minimal transparency beyond the basic function.

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 sentence with no extraneous information, achieving maximum conciseness and front-loading the purpose effectively.

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 the tool's simplicity (one parameter, no output schema), the description is adequate but leaves gaps: no indication of output format, whether the summary is real-time or cached, or any usage constraints. It meets minimum requirements but is not fully comprehensive.

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?

The input schema has 100% description coverage for the single parameter ('Contact ID'). The description adds no additional meaning beyond what the schema already provides, so a baseline score of 3 is appropriate.

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 indicates the tool retrieves an AI-generated summary of a contact, distinguishing it from raw data retrieval tools like spix_contact_show or history tools like spix_contact_history. The verb+resource is specific, but could better clarify what the summary includes.

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 on when to use this tool versus alternatives (e.g., spix_contact_show, spix_call_summary). The description lacks context for preferring this over sibling tools.

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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