Skip to main content
Glama
Sign In
local-spark-mcp

local-spark-mcp

by methodify
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

mount_table

Explicitly register a Fabric Delta table with a lakehouse and table name for use in code execution without auto-mounting.

Instructions

Explicitly register one Fabric Delta table as <lakehouse>.<table>. Usually unnecessary — run_sql auto-mounts referenced tables — but useful to pre-register a table for use in run_code.

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>",
      "table": "<table>"
    },
    "name": "mount_table"
  }
}

Input Schema

NameRequiredDescriptionDefault
tableYes
lakehouseYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior3/5

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

With no annotations, the description carries full burden. It explains the registration action and the naming pattern, but does not disclose idempotency, persistence, error conditions, or side effects. This is minimal but sufficient for a conceptually simple registration operation.

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 sentences, front-loaded with the action and immediately followed by practical guidance. Every word contributes; no redundancy or 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?

The description fully covers the tool's purpose, typical use case, and relationship to sibling tools. Given the low complexity (2 required string params, no nested objects) and existing output schema, nothing essential is missing.

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?

Schema coverage is 0% and there are only 2 string parameters. The description adds meaning by showing the pattern `<lakehouse>`.`<table>`, directly mapping to the parameter names and clarifying that 'table' is a Fabric Delta table and 'lakehouse' is the lakehouse context.

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 'register' with a clear resource 'one Fabric Delta table' as `<lakehouse>`.`<table>`. It distinguishes itself from run_sql and mount_lakehouse by explaining it pre-registers a table for run_code, contrasting with run_sql's auto-mounting.

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

Usage Guidelines5/5

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

It explicitly states when to use ('useful to pre-register a table for use in run_code') and when not needed ('Usually unnecessary — run_sql auto-mounts referenced tables'). The alternative tool is named and contrasted clearly.

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

Deploy Server

Other Tools