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local-spark-mcp

local-spark-mcp

by methodify
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

mount_lakehouse

Mount all tables from a lakehouse into the Spark catalog as qualified table names. Ideal for bulk registration before querying.

Instructions

Mount ALL tables in a lakehouse as <lakehouse>.<table>. Convenient, but can register many tables at once.

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": "mount_lakehouse"
  }
}

Input Schema

NameRequiredDescriptionDefault
lakehouseYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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 discloses that this mounts all tables and can register many at once, which is a key behavioral trait and potential risk. It does not mention reversibility or permissions, but the main caveat is covered.

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 short sentences, front-loaded with the core action, no redundant information. Every word earns its place.

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

Completeness4/5

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

With an output schema present and a single simple parameter, the description sufficiently covers purpose, scope, and a side effect. It doesn't address edge cases like existing mounts, but for a bulk mount tool this is adequate.

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 schema has no descriptions (0% coverage), but the description uses the `<lakehouse>` placeholder to explain the parameter's role in the naming convention. This adds meaningful context to an otherwise bare 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 a specific verb ('Mount') and resource ('ALL tables in a lakehouse'), and clearly defines the naming convention (`<lakehouse>`.`<table>`). This distinguishes it from sibling tools like mount_table, which mounts a single table.

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 implies when to use this tool (when you want all tables) and warns about the side effect of registering many tables at once. It does not explicitly name alternatives like mount_table, but the contrast is clear from the phrase 'Mount ALL tables'.

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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