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truaxki
by truaxki

list_tables

Retrieve all database tables to monitor statistical variations in conversation structure and log unusual events using the MCP Variance Log server.

Instructions

List all tables in the database

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler for the 'list_tables' tool: executes SQL query to retrieve all table names from SQLite metadata and returns results as text.
    if name == "list_tables":
        results = db._execute_query(
            "SELECT name FROM sqlite_master WHERE type='table'"
        )
        return [types.TextContent(type="text", text=str(results))]
  • Registration of the 'list_tables' tool in the list_tools handler, including its description and empty input schema.
    types.Tool(
        name="list_tables",
        description="List all tables in the database",
        inputSchema={
            "type": "object",
            "properties": {}
        }
    ),
  • Input schema for 'list_tables' tool: empty object since no parameters required.
        inputSchema={
            "type": "object",
            "properties": {}
        }
    ),
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states 'List all tables' but does not disclose behavioral traits such as whether it requires permissions, how results are formatted (e.g., pagination), or if it's read-only. This is inadequate for a tool with zero annotation coverage.

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 a single, efficient sentence with zero waste. It is front-loaded and directly conveys the core action, making it highly concise and well-structured for its purpose.

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 and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., permissions, format) and does not explain return values, which is insufficient for a tool that might have complexity in how tables are listed or accessed.

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 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not add parameter details, and since there are no parameters, it meets the baseline for this scenario without compensation required.

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 ('List') and resource ('tables in the database'), making the purpose unambiguous. However, it does not differentiate from potential siblings like 'describe_table' or 'read_query', which might also involve table information, so it doesn't reach the highest score.

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. With siblings like 'describe_table' (for details on a specific table) and 'read_query' (which might list tables via SQL), there is no explicit or implied context for choosing this tool, leaving a significant gap.

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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