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local-spark-mcp

local-spark-mcp

by methodify
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

list_lakehouses

Discover the Fabric lakehouses available in your current session. Each is registered as a Spark database with tables mounted on demand.

Instructions

List the Fabric lakehouses available in this session. Each is registered as a Spark database; its tables are mounted on demand.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_lakehouses"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that lakehouses are registered as Spark databases and tables are mounted on demand, implying listing does not mount tables. However, it does not state read-only behavior, permissions, or effect of no lakehouses, so moderate transparency.

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?

Two concise sentences, front-loaded with the action, no filler.

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

Completeness5/5

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

Given the zero-parameter interface and presence of an output schema, the description sufficiently explains purpose and key behavioral context. It covers what is listed and the on-demand mounting relationship, making it complete for a simple list tool.

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 tool has 0 parameters, so baseline is 4. The description adds no parameter-specific detail but explains the domain concept (lakehouses as databases), which is sufficient.

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 clearly states the tool lists Fabric lakehouses available in the session, using the specific verb 'List' and resource 'Fabric lakehouses'. The additional detail about Spark database registration distinguishes it from list_tables and other sibling tools.

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 does not explicitly state when to use this tool over alternatives, but implies usage for viewing session-available lakehouses. It mentions they are registered as Spark databases and tables mounted on demand, providing context but no explicit exclusions or alternative tool recommendations.

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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