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Coda Delete Rows

coda_delete_rows
Destructive

Delete multiple rows from a Coda table in one call. List row IDs first with coda_list_rows, then permanently remove them and receive a requestId for async tracking.

Instructions

Delete multiple rows from a Coda table in a single call.

Permanently removes all specified rows. This is irreversible. List the rows first with coda_list_rows to get their IDs. Returns a requestId for async mutation tracking. Prefer this over calling coda_delete_row in a loop for better performance and rate limit efficiency.

Input Schema

NameRequiredDescriptionDefault
doc_idYesThe doc ID containing the table
row_idsYesList of row IDs to delete
table_id_or_nameYesTable ID or name

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.12.1
    • changedOutput schema / (root)
      Previous value: -{
      -  "properties": {
      -    "result": {
      -      "type": "string"
      -    }
      -  },
      -  "required": [
      -    "result"
      -  ],
      -  "type": "object",
      -  "x-fastmcp-wrap-result": true
      -}New value: +null
  2. First observedv0.4.14

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true, so the safety profile is partly covered, but the description adds genuinely new context: irreversibility ('Permanently removes all specified rows. This is irreversible') and the async contract ('Returns a requestId for async mutation tracking'). It stops short of naming required permissions or whether partial failures roll back.

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?

Four short sentences, front-loaded with the destructive scope, then prerequisite, return behavior, and the alternative. Every sentence carries distinct information; no 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?

With no output schema, the description correctly supplies the return value (requestId for async tracking), the prerequisite lookup, the irreversibility warning, and the sibling preference. An agent has everything needed to call and follow up correctly.

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 description coverage is 100%, so the baseline is 3. The description adds only one piece of parameter meaning — that row_ids values come from coda_list_rows — and says nothing about doc_id or table_id_or_name formats, so it does not exceed 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?

States a specific verb+resource with an explicit batch scope ('Delete multiple rows from a Coda table in a single call'), which immediately separates it from the singular coda_delete_row sibling. An agent can select correctly without opening the schema.

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

Usage Guidelines5/5

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

Gives an explicit prerequisite ('List the rows first with coda_list_rows to get their IDs') and an explicit alternative with the condition that selects it ('Prefer this over calling coda_delete_row in a loop for better performance and rate limit efficiency'). Nothing is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.