Quillm

Add or update rows

upsert_rows

DestructiveIdempotent

Adds or updates rows in a dataset. This is how you "add new data" or "correct a number": every view reading the dataset shows the change immediately, with no view edit needed. Rows are matched on the dataset's key columns: a new key inserts, an existing key is merged (only the columns you send are overwritten). Safe to re-run. Values must match the column types (number columns take JSON numbers, not strings like "$1,200"; date columns take ISO strings like "2026-03-01" or "2026-03"). Unknown or not applicable is null, never "" or 0. It writes only to this Quillm workspace and calls no other service.

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": {
      "dataset": "<dataset>",
      "rows": "<rows>"
    },
    "name": "upsert_rows"
  }
}

Input Schema

Name	Required	Description
`note`	No	Optional changelog note, e.g. "October close" or "corrected refunds".
`rows`	Yes	Row objects keyed by column name. Must include the key column(s).
`dataset`	Yes	The dataset's name, e.g. "ad_spend_daily".

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 3, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already flag idempotentHint and destructiveHint, and the description adds real context beyond them: views reflect the change immediately with no view edit, merge only overwrites columns you send, safe to re-run, value-type requirements, null conventions, and the fact that it writes only to this workspace and calls no other service. This is unusually rich behavioral disclosure 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.

Conciseness4/5

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

Front-loaded with the core action, then layered with semantics and constraints in roughly six tight sentences; the quoted trigger phrases aid routing. It is slightly dense and could trim a clause or two, but nothing is filler.

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

Completeness5/5

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

For a three-parameter mutation tool with no output schema but full annotation coverage, the description supplies everything needed to call it correctly: matching behavior, merge semantics, idempotency, type rules, null handling, and side-effect scope.

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 already 100%, so the baseline is 3, but the description adds genuine meaning not in the schema: rows are matched on key columns, only sent columns are overwritten, and values must match column types (JSON numbers not '$1,200', ISO dates). It does not describe the note parameter or the 5000-row limit, so it is strong rather than 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?

States a specific verb+resource (adds or updates rows in a dataset) and immediately clarifies the upsert semantics. It is clearly distinguishable from siblings like delete_rows, query_dataset, and update_dataset without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete trigger phrasing — this is how you 'add new data' or 'correct a number' — and explains the key-matching rule that decides insert vs merge. It does not name sibling alternatives or state when-not to use it, so it stops short of the top score.

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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