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Databar MCP Server

Databar MCP Server

Official
by databar-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

get_table_columns

Retrieve column names, types, and identifiers for any table using its UUID.

Instructions

Get all columns defined on a table. Returns column names, types, and identifiers.

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": {
      "table_uuid": "<table_uuid>"
    },
    "name": "get_table_columns"
  }
}

Input Schema

NameRequiredDescriptionDefault
table_uuidYesThe UUID of the table

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.4.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It describes the basic behavior (returns column info) but does not disclose potential errors, authorization needs, or whether it includes hidden columns. Adequate but not comprehensive.

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 a single sentence with no extraneous information. It is front-loaded and efficient.

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?

Given the tool's simplicity (1 param, no output schema), the description is reasonably complete. It specifies what the tool returns. It could mention that the table must exist or error handling, but overall it suffices.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a single parameter well-described as 'The UUID of the table'. The description adds no extra information beyond the schema, so baseline score applies.

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 explicitly states 'Get all columns defined on a table' and specifies the return value ('column names, types, and identifiers'). It clearly distinguishes from sibling tools that create, delete, or rename columns.

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 implies usage for reading table schema but does not explicitly state when to use it over alternatives, nor does it mention prerequisites like table existence or when not to use it.

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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