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create_column

Add a typed column to a board, choosing from text, number, date, status, relation, and more. Enforce validation rules like unique, ranges, and patterns; dropdowns reject out-of-list values unless allowed.

Instructions

Add a typed column to a board. Valid types: text, rich_text, number, status, date, datetime, duration, people, checkbox, dropdown, label, priority, link, email, phone, relation, lookup, rollup, formula, rating, currency, file. Choose by meaning — date for dates, phone for phones, number/currency for amounts, dropdown/status (with settings.options as an array of labels) for closed choices; text is for free text only. An obvious name/type mismatch is rejected with the suggested type; pass force:true to override. Rules go in settings.validation: { unique, min, max, minLength, maxLength, pattern, patternMessage } — enforced on every write (UI, MCP, App API). Closed choices (dropdown/status) reject values outside settings.options unless settings.allowCustom is true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
typeYes
forceNoCreate the column even when the name suggests a different type
boardIdYes
settingsNoType-specific settings. For dropdown/status/priority: options, either as labels ["A","B"] or as full objects [{value,label,color}] — labels are expanded server-side, and colors are assigned if you do not supply them.
projectIdYes
isRequiredNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.7.0

TDQS

A4.3/5.0
Behavior5/5

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

Beyond the annotations, the description discloses important behaviors: name/type mismatch rejection with a suggested type, force:true override, validation enforcement on every write across UI/MCP/App API, and closed-choice restrictions on dropdown/status values. This gives the agent a precise model of side effects and edge cases.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-structured, with the core purpose first and then progressively more specific behavioral details. It is longer than typical, but the complexity of type selection and settings justifies the length; most sentences carry unique information.

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?

For a tool with seven parameters, nested settings, and no output schema, the description covers the essential invocation context: valid types, settings structure, validation behavior, and custom-option handling. It does not describe success return values or relationships between complex types, but the tool can be invoked correctly with the information provided.

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 only 29%, but the description compensates by explaining the type parameter at length, detailing settings.options, settings.validation, force, and allowCustom. projectId, boardId, name, and isRequired remain implicit, but their meanings are reasonably inferable from names and the schema.

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 opens with a specific verb and resource: 'Add a typed column to a board.' This clearly separates the tool from sibling operations like update_column, delete_column, and reorder_columns, so an agent can identify the correct tool without confusion.

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 gives strong guidance on how to choose column types and when to use force, but it does not explicitly state when to use create_column versus update_column/delete_column. The creation intent is clear, yet the contrast with alternatives is left implicit rather than stated.

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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