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dstreefkerk

ms-sentinel-mcp-server

by dstreefkerk

llm_instructions_get

Retrieve usage instructions for Microsoft Sentinel MCP Server tools to understand how to interact with security data and operations.

Instructions

Retrieve the LLM usage instructions for the Sentinel MCP Server. Use this tool first before all other tools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kwargsYes

Implementation Reference

  • The LLMInstructionsGetTool class defines the tool with name 'llm_instructions_get' and implements the 'run' method that reads and returns the LLM instructions from docs/llm_instructions.md.
    class LLMInstructionsGetTool(MCPToolBase):
        """Tool for retrieving the LLM usage instructions for the Sentinel MCP Server."""
    
        name = "llm_instructions_get"
        description = (
            "Retrieve the LLM usage instructions for the Sentinel MCP Server. "
            "Use this tool first before all other tools."
        )
    
        async def run(self, ctx, **kwargs) -> Any:
            """
            Retrieve the LLM usage instructions for the Sentinel MCP Server.
    
            Args:
                ctx: The tool context (unused).
                **kwargs: Optional arguments (unused).
    
            Returns:
                dict: {
                    'content': raw markdown content of docs/llm_instructions.md,
                    'error': error message if file cannot be read
                }
            """
    
            # Defensive: handle string, None, or dict for kwargs (even if unused)
    
            # No parameters to extract, but we'll normalize kwargs for consistency
            # using the centralized parameter extraction from MCPToolBase
            _ = self._extract_param(kwargs, "")
            try:
                llm_path = Path(__file__).parent.parent / "docs" / "llm_instructions.md"
                content = llm_path.read_text(encoding="utf-8")
                return {"content": content}
            except Exception as e:
                return {"error": f"Failed to read LLM instructions: {e}"}
  • The register_tools function registers the LLMInstructionsGetTool (and other doc tools) to the MCP server instance.
    def register_tools(mcp):
        """Register all documentation tools with the given MCP server instance."""
        ToolDocsListTool.register(mcp)
        ToolDocsGetTool.register(mcp)
        ToolDocsSearchTool.register(mcp)
        LLMInstructionsGetTool.register(mcp)
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 retrieval but doesn't disclose behavioral traits like authentication needs, rate limits, response format, or potential side effects. The guidance to use it first hints at initialization, but lacks detail on what the instructions contain or how they affect subsequent tool usage.

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 two concise sentences with zero waste. It's front-loaded with the purpose and followed by usage guidance, making it highly efficient and well-structured.

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 no annotations, 0% schema coverage, no output schema, and a parameter-heavy context (1 required param), the description is incomplete. It lacks details on parameters, return values, and behavioral aspects, making it insufficient for an agent to use the tool effectively beyond the high-level guidance.

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

Parameters1/5

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

Schema description coverage is 0%, and the description provides no information about the single required parameter 'kwargs'. It doesn't explain what 'kwargs' represents, its format, or how it should be used, leaving the parameter completely undocumented.

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 'retrieve' and the resource 'LLM usage instructions for the Sentinel MCP Server'. It specifies what the tool does, though it doesn't explicitly differentiate from sibling tools, which are unrelated to LLM instructions.

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

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states 'Use this tool first before all other tools', providing clear when-to-use guidance. It implies this is a prerequisite for other operations, though it doesn't name specific alternatives or exclusions.

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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