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Databar MCP Server

Databar MCP Server

Official
by databar-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

upsert_rows

Insert new rows or update existing ones in a Databar table by matching on a unique key, up to 100 rows per request.

Instructions

Insert or update rows by matching key (max 100 per request).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "table_uuid": "<table_uuid>",
      "rows": "<rows>"
    },
    "name": "upsert_rows"
  }
}

Input Schema

NameRequiredDescriptionDefault
table_uuidYesThe UUID of the table
rowsYesArray of upsert operations (max 100)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.4.0

TDQS

A3.7/5.0
Behavior3/5

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

The description discloses the core behavior (upsert by key) and a constraint (max 100). However, as no annotations are provided, it omits details like whether updates are partial or full, what happens on duplicate keys, or required permissions. It covers basic transparency but lacks depth.

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 a single, efficient sentence that conveys the operation, mechanism, and constraint. Every word is meaningful and well-placed, with no redundancy or unnecessary detail.

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 tool's simplicity and the absence of an output schema, the description covers the essentials. However, it lacks details on response format, error behavior, and how it compares to sibling tools, which would make it more complete for an AI agent.

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 already describes both parameters, but the description adds critical context: 'by matching key' clarifies that the 'key' property in the rows array is used for matching existing rows. This goes beyond the schema's property descriptions, which are minimal.

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 operation: 'Insert or update rows by matching key'. It specifies the resource (rows) and the action (upsert), distinguishing it from siblings like 'create_rows' (insert only) and 'patch_rows' (update only).

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?

The description provides no explicit guidance on when to use this tool over alternatives such as 'create_rows' or 'patch_rows'. It does not mention prerequisites, edge cases, or scenarios where this tool is preferred, leaving the agent without decision support.

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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