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databricks-mcp

by ChrisChoTW

list_tables

Retrieve all tables within a specified Databricks schema and catalog to explore available data structures.

Instructions

List tables in the specified schema

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
catalog_nameYes
schema_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler implementation for the `list_tables` MCP tool, which sanitizes identifiers and executes a SQL query.
    @mcp.tool
    def list_tables(ctx: Context, catalog_name: str, schema_name: str) -> List[Dict[str, Any]]:
        """List tables in the specified schema"""
        catalog = safe_identifier(catalog_name, "catalog_name")
        schema = safe_identifier(schema_name, "schema_name")
        return execute_sql(ctx, f"SHOW TABLES IN {catalog}.{schema}")
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states it's a list operation but doesn't describe return format, pagination, permissions needed, rate limits, or error conditions. This leaves significant gaps for a tool that interacts with database schemas.

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 that gets straight to the point with zero wasted words. It's appropriately sized for a simple list operation and front-loads the essential information.

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

Completeness3/5

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

Given the tool has an output schema (which handles return values) and relatively simple functionality, the description covers the basic purpose adequately. However, with no annotations and poor parameter documentation, it leaves important behavioral and usage context unspecified for a database interaction tool.

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?

With 0% schema description coverage for both required parameters, the description only mentions 'specified schema' which partially covers 'schema_name' but completely ignores 'catalog_name'. It doesn't explain what these parameters represent, their format, or valid values.

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 action ('List') and resource ('tables in the specified schema'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'list_catalogs' or 'list_schemas', but the specificity of 'tables' provides some implicit distinction.

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 'search_tables' or 'get_table_detail'. It mentions the schema parameter but doesn't explain prerequisites, context, or exclusions for usage.

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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