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get_db_system

Retrieve detailed information about Oracle Cloud Infrastructure database systems to monitor configurations, check status, and manage resources.

Instructions

Get DB System details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
db_system_idYes

Implementation Reference

  • MCP tool handler 'mcp_get_db_system' that calls the helper function with OCI database client to retrieve and return DB System details.
    @mcp.tool(name="get_db_system")
    @mcp_tool_wrapper(
        start_msg="Getting DB System {db_system_id}...",
        success_msg="Retrieved DB System successfully",
        error_prefix="Error getting DB System"
    )
    async def mcp_get_db_system(ctx: Context, db_system_id: str) -> Dict[str, Any]:
        """Get DB System details."""
        return get_db_system(oci_clients["database"], db_system_id)
  • Helper function that fetches DB System details using OCI SDK and formats the response dictionary.
    def get_db_system(database_client: oci.database.DatabaseClient, db_system_id: str) -> Dict[str, Any]:
        """Get DB System details."""
        try:
            d = database_client.get_db_system(db_system_id).data
            return {
                "id": d.id,
                "display_name": d.display_name,
                "lifecycle_state": d.lifecycle_state,
                "shape": d.shape,
                "database_edition": getattr(d, "database_edition", None),
                "availability_domain": getattr(d, "availability_domain", None),
                "time_created": str(getattr(d, "time_created", "")),
                "subnet_id": getattr(d, "subnet_id", None),
                "compartment_id": d.compartment_id,
                "node_count": getattr(d, "node_count", None),
                "version": getattr(d, "version", None),
                "cpu_core_count": getattr(d, "cpu_core_count", None),
                "data_storage_size_in_gb": getattr(d, "data_storage_size_in_gb", None),
                "listener_port": getattr(d, "listener_port", None),
                "scan_dns_record_id": getattr(d, "scan_dns_record_id", None),
                "ssh_public_keys": getattr(d, "ssh_public_keys", None),
            }
        except Exception as e:
            logger.exception(f"Error getting DB System: {e}")
            raise
Behavior1/5

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. The description only states what the tool does ('Get DB System details') without mentioning whether this is a read-only operation, what permissions are required, whether it has side effects, rate limits, error conditions, or what format the details are returned in. For a tool with zero annotation coverage, this is completely inadequate.

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 extremely concise at just three words, which is appropriately brief for a simple-sounding tool. However, this brevity comes at the cost of being under-specified rather than efficiently informative. While it's front-loaded with the core action, it lacks the necessary detail that would make it truly helpful.

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

Completeness1/5

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

Given the complexity of database systems in cloud environments, no annotations, no output schema, and 0% parameter documentation coverage, the description is completely inadequate. It doesn't explain what 'details' are returned, how this differs from list operations, what authentication is required, or any behavioral characteristics. For a tool that likely interacts with production database infrastructure, this minimal description poses significant risks for correct agent usage.

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

Parameters2/5

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

The input schema has 1 parameter (db_system_id) with 0% schema description coverage, meaning the parameter is completely undocumented in the schema. The description provides no information about this parameter - what a DB System ID is, where to find it, its format, or examples. While there's only one parameter, the description fails to compensate for the complete lack of schema documentation, leaving the agent guessing about required input.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Get DB System details' is a tautology that essentially restates the tool name 'get_db_system' without adding meaningful specificity. It mentions the resource ('DB System') but lacks detail about what specific details are retrieved or how this differs from sibling tools like 'get_database' or 'list_db_systems'. This provides minimal value beyond the name itself.

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

Usage Guidelines1/5

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. With numerous sibling tools like 'get_database', 'list_db_systems', 'get_db_node', and 'get_autonomous_database', there is no indication of when this specific DB System retrieval is appropriate, what prerequisites exist, or what distinguishes it from other database-related tools. This leaves the agent with no 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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