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databar-ai

Databar MCP Server

Official
by databar-ai

create_rows

Insert rows into a Databar table (max 100). Auto-create new text columns when adding data with unknown column names.

Instructions

Insert new rows into a table (max 100 per request). To add new columns to an existing table, set options.allow_new_columns to true — any column name in fields that does not exist yet will be auto-created as a text column.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_uuidYesThe UUID of the table
rowsYesArray of rows to insert (max 100). Each row has a fields object keyed by column name.
optionsNo
Behavior3/5

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

With no annotations, the description partially carries the burden. It discloses the 100-row limit and column auto-creation behavior, but omits common traits like error handling (e.g., invalid table_uuid), atomicity, idempotency, and permission requirements. More details are needed for full transparency.

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 extremely concise (two sentences) and front-loads the primary action and limit. Every word adds value; no fluff or repetition. It efficiently conveys the core functionality and a key option.

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 the absence of an output schema, the description should mention what is returned (e.g., success status or row IDs). It also lacks error scenarios and prerequisites (e.g., table must exist). It covers the main insertion mechanism but misses completeness for a well-rounded understanding.

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?

The description adds meaning beyond the schema for 'rows' (max 100) and 'options.allow_new_columns' (auto-creates text columns). However, it does not explain 'options.dedupe' (undocumented in schema) or the structure of 'fields' beyond key-value pairs. Schema coverage is 67%, so moderate compensation is provided but not complete.

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 purpose: 'Insert new rows into a table' with a max limit of 100, which distinguishes it from sibling tools like 'delete_rows' or 'upsert_rows'. The verb 'insert' and resource 'rows' are specific and 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?

The description explains when to use 'options.allow_new_columns' to auto-create columns, noting it's the only API method for adding columns. However, it lacks explicit guidance on when not to use this tool (e.g., for updates, prefer 'patch_rows' or 'upsert_rows') and does not compare to alternatives like 'create_column'.

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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