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SheetForge MCP

SheetForge MCP

by iHeldan
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

append_table_rows

Destructive

Append dictionary-shaped rows to a worksheet by matching each row’s keys to the header row, with optional dry-run and structure checks.

Instructions

Append dictionary-shaped rows by matching worksheet headers on a worksheet dataset.

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": {
      "filepath": "<filepath>",
      "sheet_name": "<sheet_name>",
      "rows": "<rows>"
    },
    "name": "append_table_rows"
  }
}

Input Schema

NameRequiredDescriptionDefault
rowsYes
dry_runNo
filepathYesReuse supplied server path; NEVER prepend client cwd. Rooted: relative OK; unrooted stdio: absolute, ask if missing.
header_rowNo
sheet_nameYes
include_changesNo
allow_structure_changeNo
expected_structure_tokenNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.14.0
    • addedInput schema / properties / filepath / description
      Added value: +"Reuse supplied server path; NEVER prepend client cwd. Rooted: relative OK; unrooted stdio: absolute, ask if missing."
  2. First observedv0.8.0

TDQS

C2.7/5.0
Behavior2/5

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

The description adds one genuine behavioral fact — rows are dicts matched against existing worksheet headers — but annotations already flag destructiveHint=true, and the description never explains what makes this destructive, that appending may extend the sheet, or what allow_structure_change/expected_structure_token guard against. For a destructive write with an 8-parameter surface, that is a significant disclosure gap.

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?

A single front-loaded sentence with no filler; the core mechanism is stated first. Its brevity is efficient, though it borders on under-specification for the tool's complexity rather than being verbose.

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?

With 8 parameters, a destructive mutation, low schema coverage, and no output schema to explain results, the description leaves the agent without critical context — notably the dry-run preview path and the structure-change safety token. What is present is accurate but far from sufficient for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 13% (just filepath), so the description must carry the load and largely does not. The phrase 'dictionary-shaped rows by matching worksheet headers' usefully clarifies the required `rows` shape, but dry_run, header_row, include_changes, allow_structure_change, and expected_structure_token get no explanation at all.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (append), resource (rows), and mechanism (matching worksheet headers), which is enough for an agent to know this writes new records rather than updating existing ones. It does not explicitly distinguish itself from close siblings like append_excel_table_rows or upsert_excel_table_rows, leaving the 'worksheet dataset' vs 'excel table' boundary to be inferred.

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?

There is no when-to-use guidance, no statement of when to prefer this over append_excel_table_rows, upsert_excel_table_rows, update_rows_by_key, or write_data_to_excel. Nothing tells the agent when dry_run or expected_structure_token should be employed.

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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