Skip to main content
Glama
ChrisChoTW

databricks-mcp

by ChrisChoTW

list_catalogs

Retrieve all available catalogs from Databricks SQL to organize and access data resources.

Instructions

List all catalogs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The `list_catalogs` function is defined and decorated as an MCP tool, executing "SHOW CATALOGS" via `execute_sql`.
    @mcp.tool
    def list_catalogs(ctx: Context) -> List[Dict[str, Any]]:
        """List all catalogs"""
        return execute_sql(ctx, "SHOW CATALOGS")
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 of behavioral disclosure. It states it's a list operation, implying read-only, but doesn't cover aspects like pagination, rate limits, authentication needs, or what 'all' means (e.g., all accessible catalogs). This leaves significant gaps for an agent to understand the tool's behavior.

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 extremely concise with a single sentence 'List all catalogs', which is front-loaded and wastes no words. It efficiently conveys the core purpose without unnecessary elaboration, making it easy for an agent to parse quickly.

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 0 parameters, 100% schema coverage, and an output schema exists, the description is minimally adequate. However, with no annotations and multiple sibling list tools, it lacks context about how catalogs fit into the hierarchy or what the output entails, leaving room for confusion in a rich server environment.

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 doesn't add param info, which is appropriate here, but it also doesn't imply any hidden parameters or constraints, keeping it straightforward for a no-param tool.

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

Purpose3/5

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

The description 'List all catalogs' clearly states the action (list) and resource (catalogs), but it's vague about scope or format. It doesn't distinguish from siblings like 'list_schemas' or 'list_tables' beyond the resource name, leaving ambiguity about what a 'catalog' entails in this context.

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?

No guidance is provided on when to use this tool versus alternatives. With siblings like 'list_schemas' and 'list_tables', the description doesn't explain if catalogs are a higher-level container or how they relate, nor does it mention prerequisites 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.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ChrisChoTW/databricks-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server