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jason-ung

starrocks-mcp

by jason-ung

list_databases

Retrieve all database names from StarRocks for schema discovery and data analysis, enabling users to explore available data sources.

Instructions

List all databases in StarRocks

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The actual implementation of the list_databases logic, executing a SQL query on the StarRocks instance.
    def list_databases(self) -> List[str]:
        """List all databases."""
        results = self.execute_query("SHOW DATABASES")
        return [row["Database"] for row in results]
  • The MCP tool handler in table_tools.py that calls client.list_databases().
    if name == "list_databases":
        databases = client.list_databases()
        return [
            TextContent(
                type="text",
                text=f"Found {len(databases)} databases:\n\n{format_json(databases)}",
            )
        ]
  • Registration of the "list_databases" tool definition.
    Tool(
        name="list_databases",
        description="List all databases in StarRocks",
        inputSchema={"type": "object", "properties": {}},
    ),
Behavior3/5

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

With no annotations provided, the description carries the full disclosure burden. While 'List' implies read-only behavior and 'all' defines scope, it omits details about return format, pagination limits, or required permissions to view all databases.

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?

Five words, zero redundancy. Front-loaded with action verb ('List') followed immediately by target resource. Every word earns its place.

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 zero parameters and no annotations, the description adequately covers the tool's function. Minor gap: without an output schema, it could briefly mention what data is returned (database names vs metadata), but the scope is sufficiently clear for a simple listing operation.

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?

Input schema has zero parameters. Per rubric rules, zero parameters establishes a baseline score of 4. The description correctly implies no filtering parameters are accepted.

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

Purpose5/5

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

The description provides a specific verb ('List'), clear resource ('databases'), and scope ('all...in StarRocks'). It clearly distinguishes from sibling 'list_tables' (databases vs tables) and 'describe_table'/'execute_query' (listing vs describing/executing).

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

Usage Guidelines3/5

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

The description states what the tool does but provides no explicit guidance on when to use this versus siblings like 'list_tables' (e.g., 'use this first to find database names before listing tables') or any prerequisites needed to access database metadata.

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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