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aficiomaquinas

mcp-baserow-schema

create_row

Create a row in a Baserow table by supplying field values as a JSON object with human-readable field names.

Instructions

Create a new row in a table. Provide field values as a JSON object with field names as keys. Uses human-readable field names.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesField values as key-value pairs. Keys are field names (when using user_field_names=true). Example: {"Name": "Alice", "Email": "alice@example.com"}
table_idYesThe table ID to create the row in.
Behavior2/5

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

With no annotations, the description carries the full burden for behavioral disclosure. It mentions human-readable field names but does not explain what happens after creation (return value), potential errors, authentication requirements, or idempotency. This is a significant gap for a mutation tool.

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 three short sentences, front-loaded with the primary action, and every sentence adds relevant information. No wasted words or redundant elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description should explain return values, but it does not. For a simple create operation, it also lacks information about error handling or success indications. The tool has only two parameters, so the description is somewhat complete, but the missing return behavior hurts the overall completeness.

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%, so the baseline is 3. The description adds minimal value by repeating that field values are provided as a JSON object and noting human-readable names, which is already implied by the schema's example. It does not clarify the data object structure beyond 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 clearly states the tool's action ('Create a new row in a table') with a specific verb and resource. It distinguishes from sibling tools like update_row, delete_row, and batch_create_rows by emphasizing single-row creation.

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 some usage context (provide field values as JSON object, human-readable field names) but does not explicitly state when to use this tool versus batch_create_rows or other alternatives. There are no exclusions or alternative tool mentions, so usage is implied rather than explicit.

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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