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aficiomaquinas

mcp-baserow-schema

create_field

Add a new column to any Baserow table by specifying its name, type, and optional settings. Streamline schema changes directly from your MCP environment.

Instructions

Create a new field (column) in a table. The 'type' parameter must be one of: text, long_text, url, email, number, rating, boolean, date, last_modified, last_modified_by, created_on, created_by, duration, link_row, file, single_select, multiple_select, phone_number, formula, count, rollup, lookup, multiple_collaborators, uuid, autonumber, password, ai.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe name for the new field (max 255 chars).
typeYesThe field type. Common types: text, long_text, number, boolean, date, single_select, multiple_select, link_row, url, email, phone_number, formula, rating, duration, file, uuid, autonumber.
table_idYesThe table ID to add the field to.
field_optionsNoOptional type-specific field configuration (e.g., {text_default: 'hello', description: 'desc'}).
Behavior3/5

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

With no annotations, the description bears full responsibility for disclosing behavior. It adds the critical constraint that 'type' must be one of the listed enum values, which is valuable. However, it does not mention side effects such as altering the table schema, whether field_options is required for certain types, or what the response will contain.

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 a single, front-loaded sentence that states the purpose before listing the allowed types. The list is lengthy but necessary since the schema does not enumerate all valid types. There is no redundant filler, so it is appropriately concise for the information conveyed.

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?

The tool is moderately complex due to the many field types and the field_options parameter, but the description does not explain how to configure type-specific options or what the API returns (no output schema). The schema covers parameter descriptions well, but the lack of behavioral context around field_options and response shape leaves the description incomplete.

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 covers all four parameters, but the description adds essential semantics for the 'type' parameter by listing the complete set of allowed values, whereas the schema only provides a partial list of 'common types'. This extra information is necessary for correct parameter selection, going beyond the schema baseline.

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 'Create a new field (column) in a table' with a specific action and resource, distinguishing it from sibling tools like update_field and delete_field. The verb 'create' and the explicit mention of the resource and target make the purpose unambiguous.

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?

No explicit guidance is given regarding when to use this tool versus alternatives like update_field, delete_field, or list_fields. The purpose implies usage but the description does not mention prerequisites, exclusions, or alternative tools, leaving the agent to infer the use case from nomenclature.

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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