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JLKmach

ServiceNow MCP Server

by JLKmach

list_tool_packages

View available tool packages and identify the currently loaded one in the ServiceNow MCP Server to manage API integrations.

Instructions

Lists available tool packages and the currently loaded one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
random_stringYesDummy parameter for no-parameter tools

Implementation Reference

  • The core handler function that implements the logic for the 'list_tool_packages' tool. It lists the currently loaded package and all available packages from the configuration.
    def _list_tool_packages_impl(self) -> Dict[str, Any]:
        """Implementation logic for the list_tool_packages tool."""
        available_packages = list(self.package_definitions.keys())
        return {
            "current_package": self.current_package_name,
            "available_packages": available_packages,
            "message": (
                f"Currently loaded package: '{self.current_package_name}'. "
                f"Set MCP_TOOL_PACKAGE env var to one of {available_packages} to switch."
            ),
        }
  • Registers the 'list_tool_packages' tool in the list_tools MCP endpoint by appending a Tool object to the tool list (only if not 'none' package). Includes the input schema definition.
    if self.current_package_name != "none":
        tool_list.append(
            types.Tool(
                name="list_tool_packages",
                description="Lists available tool packages and the currently loaded one.",
                inputSchema={
                    "type": "object",
                    "properties": {
                        "random_string": {
                            "type": "string",
                            "description": "Dummy parameter for no-parameter tools",
                        }
                    },
                    "required": ["random_string"],
                },
            )
        )
  • Dispatch logic in the call_tool handler that specifically checks for 'list_tool_packages', validates the package, calls the implementation, serializes the result, and returns it as TextContent.
    if name == "list_tool_packages":
        if self.current_package_name == "none":
            raise ValueError(
                "Tool 'list_tool_packages' is not available in the 'none' package."
            )
        result_dict = self._list_tool_packages_impl()
        serialized_string = json.dumps(result_dict, indent=2)
        # Return a list with a TextContent object
        return [types.TextContent(type="text", text=serialized_string)]
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 mentions listing 'available' and 'currently loaded' packages but doesn't disclose behavioral traits like pagination, sorting, filtering capabilities, authentication requirements, rate limits, or what format the output takes. For a read operation with no annotation coverage, this leaves significant gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple listing tool, though it could be slightly more structured by separating available vs. loaded packages more clearly.

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 low complexity (a listing operation) and 100% schema coverage, the description is minimally adequate. However, with no annotations and no output schema, it doesn't provide enough context about behavioral aspects like output format, pagination, or error conditions, leaving room for improvement.

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, with one parameter documented as a 'Dummy parameter for no-parameter tools'. The description adds no additional parameter information beyond what's in the schema, which is appropriate given the high schema coverage, resulting in the baseline score of 3.

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 verb ('Lists') and resource ('tool packages'), specifying both 'available' and 'currently loaded' packages. It distinguishes itself from sibling tools by focusing on tool packages rather than articles, users, workflows, etc., though it doesn't explicitly differentiate from other list_* tools beyond the resource type.

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. The description doesn't mention prerequisites, timing considerations, or comparisons with other tools. It simply states what the tool does without contextual usage information.

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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