Skip to main content
Glama
databar-ai

Databar MCP Server

Official
by databar-ai

create_table

Create a new table in your Databar workspace with optional name, custom column names, and empty rows. Defaults to three columns and zero rows.

Instructions

Create a new table in your Databar workspace. Optionally specify a name, column names, and number of empty rows. By default creates columns column1/column2/column3 and 0 rows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoTable name (default: "New empty table")
columnsNoColumn names. Default: ["column1","column2","column3"]. Pass empty array [] to create a table with no columns.
rowsNoNumber of empty rows to create (default: 0)
Behavior2/5

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

No annotations provided, so the description must carry the burden. It mentions creation and defaults but does not disclose whether it overwrites existing tables, requires specific permissions, or what the return value is. For a mutation tool without annotations, this is insufficient.

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 with no redundant information. Front-loaded with the primary action and quickly covers optional parameters and defaults.

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?

Given 3 optional parameters, no output schema, and many sibling tools, the description covers basic creation but lacks details on behavior (e.g., duplicate names, column uniqueness). Adequate but not rich.

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%, and the description restates defaults and parameter options. It adds minimal value beyond the schema (e.g., default column names). Baseline 3 is appropriate.

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 action ('Create a new table') and specifies the resource ('your Databar workspace'). It outlines optional parameters and defaults, distinguishing it from sibling tools like create_column or delete_table.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like create_column or upsert_rows. The description does not mention prerequisites or exclusions.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/databar-ai/databar-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server