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wagonbomb

Megaraptor MCP

by wagonbomb

list_artifacts

Browse and filter Velociraptor forensic artifacts by name, description, or type to identify relevant tools for digital investigations and threat hunting.

Instructions

List available Velociraptor artifacts.

Args: search: Optional search term to filter artifacts by name or description artifact_type: Optional type filter: 'CLIENT', 'SERVER', or 'NOTEBOOK' limit: Maximum number of artifacts to return (default 100)

Returns: List of artifacts with their names, descriptions, and types.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchNo
artifact_typeNo
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The `list_artifacts` tool implementation, decorated with `@mcp.tool()`, which validates inputs and queries the Velociraptor server for artifacts.
    @mcp.tool()
    async def list_artifacts(
        search: Optional[str] = None,
        artifact_type: Optional[str] = None,
        limit: int = 100,
    ) -> list[TextContent]:
        """List available Velociraptor artifacts.
    
        Args:
            search: Optional search term to filter artifacts by name or description
            artifact_type: Optional type filter: 'CLIENT', 'SERVER', or 'NOTEBOOK'
            limit: Maximum number of artifacts to return (default 100)
    
        Returns:
            List of artifacts with their names, descriptions, and types.
        """
        try:
            # Validate inputs
            limit = validate_limit(limit)
    
            if artifact_type and artifact_type.upper() not in ('CLIENT', 'SERVER', 'NOTEBOOK'):
                return [TextContent(
                    type="text",
                    text=json.dumps({
                        "error": f"Invalid artifact_type '{artifact_type}'",
                        "hint": "Must be one of: CLIENT, SERVER, NOTEBOOK"
                    })
                )]
    
            client = get_client()
    
            # Build the VQL query
            conditions = []
            if search:
                conditions.append(f"name =~ '{search}' OR description =~ '{search}'")
            if artifact_type:
                conditions.append(f"type = '{artifact_type}'")
    
            where_clause = f" WHERE {' AND '.join(conditions)}" if conditions else ""
            vql = f"SELECT name, description, type, parameters FROM artifact_definitions(){where_clause} LIMIT {limit}"
    
            results = client.query(vql)
    
            # Format the results
            formatted = []
            for row in results:
                artifact = {
                    "name": row.get("name", ""),
                    "description": (row.get("description", "") or "")[:200],  # Truncate long descriptions
                    "type": row.get("type", ""),
                    "has_parameters": bool(row.get("parameters")),
                }
                formatted.append(artifact)
    
            return [TextContent(
                type="text",
                text=json.dumps(formatted, indent=2)
            )]
    
        except ValueError as e:
            # Validation errors
            return [TextContent(
                type="text",
                text=json.dumps({
                    "error": str(e),
                    "hint": "Check your limit parameter value"
                })
            )]
    
        except grpc.RpcError as e:
            # gRPC errors
            error_info = map_grpc_error(e, "listing artifacts")
            return [TextContent(
                type="text",
                text=json.dumps(error_info, indent=2)
            )]
    
        except Exception:
            # Generic errors - don't expose internals
            return [TextContent(
                type="text",
                text=json.dumps({
                    "error": "Failed to list artifacts",
                    "hint": "Check Velociraptor server connection and try again"
                })
            )]
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the return structure ('List of artifacts with their names, descriptions, and types'), but lacks critical operational context such as whether the operation is read-only, if there are rate limits, or how pagination works beyond the 'limit' parameter.

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 Args/Returns structure is clear and appropriately front-loaded. The content is dense with no wasted sentences, though the structured format slightly increases length, it improves scannability for the agent.

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

Completeness4/5

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

Given the tool has 3 optional parameters and an output schema exists (mentioned in the description), the provided information is sufficient. The description adequately covers filtering capabilities and return value shape without needing to duplicate the full output schema.

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 0% description coverage (only titles), but the description effectively compensates by documenting all three parameters: 'search' filters by name/description, 'artifact_type' accepts specific enum values ('CLIENT', 'SERVER', or 'NOTEBOOK'), and 'limit' has a documented default of 100.

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 tool 'List[s] available Velociraptor artifacts' with a specific verb and resource. However, it does not explicitly distinguish this listing tool from the sibling 'get_artifact' (which retrieves a single artifact) or 'collect_artifact' (which executes an artifact).

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 like 'get_artifact' for fetching specific artifact definitions or when to prefer filtering via 'search' versus retrieving all artifacts. No prerequisites or exclusions are mentioned.

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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