local-spark-mcp

Overview Schema Source Related Servers Discussions Admin

list_tables

List Delta tables in a Fabric lakehouse to see available data before mounting them for SQL queries.

Instructions

List the Delta tables in a Fabric lakehouse. Tables are not queryable via SQL until you mount them with mount_table or mount_lakehouse.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "lakehouse": "<lakehouse>"
    },
    "name": "list_tables"
  }
}

Input Schema

Name	Required	Description	Default
`lakehouse`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 14, 2026

TDQS

A3.8/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It discloses a key behavior ('Tables are not queryable via SQL until you mount them'), which is useful. However, it does not explicitly state that listing itself is read-only, or mention behavior on missing lakehouses or return format details. Score 3 reflects adequate but not rich 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?

The description is two sentences, front-loaded with the core purpose. The second sentence adds essential context about SQL queryability and mounting without any redundancy. It is precise and efficient.

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?

For a one-parameter tool with an output schema, the description covers the basic purpose and a workflow note, but it lacks parameter semantics and any mention of prerequisites or error cases. It is minimally viable but not fully complete for correct invocation.

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?

Schema description coverage is 0%, and the description adds only the vague phrase 'in a Fabric lakehouse.' It does not specify whether the 'lakehouse' parameter expects a name, ID, or path, nor any format constraints. The agent is left guessing about how to fill in the required parameter.

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 uses the specific verb 'List' and identifies the exact resource ('Delta tables in a Fabric lakehouse'). It also distinguishes the tool from siblings by focusing on tables rather than lakehouses and by mentioning the mounting workflow.

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

Usage Guidelines4/5

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

The description provides clear context: tables are not queryable via SQL until mounted, which tells the agent when this tool is useful (for discovery before mounting). It does not explicitly name alternatives for listing lakehouses, but the mount tools are mentioned, giving workflow guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server