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by io.github.eftpmc
OverviewDiscussionsAdmin

add_columns

Add columns to a draft you run, such as a blank one a person started. Returns the new column ids for fill_cells. Needs a token.

Input Schema

NameRequiredDescriptionDefault
slugYes
columnsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / properties / columns / items / properties / choices / description
      Previous value: -"For an AI text column, the only answers it takes"New value: +"For a text column, the only values it takes, like a status list. An AI column answers with one of them"
    • addedInput schema / properties / columns / items / properties / type / description
      Added value: +"A date is written YYYY-MM-DD"
    • changedInput schema / properties / columns / items / properties / type / enum
      Previous value: -[
-  "text",
-  "number",
-  "boolean"
-]New value: +[
+  "text",
+  "number",
+  "boolean",
+  "date"
+]
  2. Changed2 schema fields changed
    • addedInput schema / properties / columns / items / properties / choices
      Added value: +{
+  "description": "For an AI text column, the only answers it takes",
+  "items": {
+    "maxLength": 80,
+    "type": "string"
+  },
+  "maxItems": 50,
+  "type": "array"
+}
    • addedInput schema / properties / columns / items / properties / instruction
      Added value: +{
+  "description": "Makes it an AI column: what to work out for each row, naming other columns in braces, like \"Classify {Pitch} by genre\". Needs proof to be optional",
+  "maxLength": 1000,
+  "type": "string"
+}
  3. First observed

TDQS

B3.4/5.0
Behavior4/5

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

Annotations already cover the mutation/safety profile (readOnlyHint=false, destructiveHint=false, idempotentHint=false), and the description adds two things not in structured data: an auth requirement ("Needs a token") and the return payload (new column ids). It stops short of mentioning limits such as the 20-column cap or what happens to existing columns.

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?

Three short sentences, front-loaded with the action and followed by return value and auth need. Every sentence carries weight; only the slightly awkward "such as a blank one a person started" phrasing costs a point.

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?

For a mutation tool with annotations covering safety and no output schema, the description covers auth, return value, and workflow placement, which is decent. It leaves the `slug` identifier and the column-count constraint unexplained, gaps an agent would hit on first call.

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?

The top-level `slug` parameter is undocumented in both the schema and the description — the phrase "a draft you run" only weakly implies it identifies the draft. The nested column properties (rule, type, choices, instruction) are well described in the schema, but the description itself adds nothing about which parameters to supply or how.

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 and resource ("Add columns") and scopes it to "a draft you run, such as a blank one a person started," which distinguishes it from create_table or add_rows. It also names the downstream consumer (fill_cells), so the agent can place it in the workflow.

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?

"Returns the new column ids for fill_cells" implies the next step in the sequence, and "such as a blank one a person started" implies when it applies. There is no explicit when-not or a named alternative to prefer over this tool, so usage is implied rather than spelled out.

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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