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IA-QA — 152 QA & Dev Tools for AI Agents

by io.github.JcJamet
OverviewDiscussionsAdmin

format_table

Read-onlyIdempotent

Convert a JSON array of objects into a Markdown table. Automatically detects columns, aligns headers, and fills missing keys with empty cells. Use when an agent needs to present structured data — tool results, model comparisons, test reports — as a readable table in a response or document.

Input Schema

NameRequiredDescriptionDefault
inputYesThe array of objects to render — a JSON string, or the array itself.
columnsNoColumn names and order (default: all keys from first row)

Output Schema

NameRequiredDescriptionDefault
rowsNo
tableNo
columnsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "input": [
+      {
+        "a": 1
+      }
+    ]
+  }
+]
  2. Changed2 schema fields changed
    • changedInput schema / properties / input / description
      Previous value: -"JSON array of objects to convert to a Markdown table"New value: +"The array of objects to render — a JSON string, or the array itself."
    • changedInput schema / properties / input / type
      Previous value: -"string"New value: +[
+  "string",
+  "object",
+  "array"
+]
  3. Changed1 schema field changed
    • addedOutput schema / properties
      Added value: +{
+  "columns": {
+    "type": "number"
+  },
+  "rows": {
+    "type": "number"
+  },
+  "table": {
+    "type": "string"
+  }
+}
  4. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "type": "object"
+}
  5. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered; the description goes further by disclosing that columns are auto-detected, headers aligned, and missing keys rendered as empty cells — defaults an agent could not infer from the schema. No detail on how nested or non-object values are handled, but for a pure transform that is minor.

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?

Two sentences, zero padding: the operation and its automatic behaviors come first, the usage trigger second. Every clause carries actionable information.

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

Completeness4/5

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

With an output schema present and annotations covering safety, the description needs only purpose, behavior, and usage — all present. A small gap remains around edge-case handling (non-object array elements, nested values), which matters for a formatting tool handling arbitrary JSON.

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%, including the 'default: all keys from first row' note for columns, so the baseline of 3 applies. The description adds only the missing-key fill behavior and nothing new about the input/columns parameters themselves.

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+resource ('Convert a JSON array of objects into a Markdown table') and adds concrete rendering behavior (column detection, header alignment, empty-cell filling). It is effectively distinguishable from siblings like json_to_csv or html_to_markdown, but never names or explicitly contrasts an alternative.

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?

'Use when an agent needs to present structured data — tool results, model comparisons, test reports — as a readable table in a response or document' gives a clear triggering context with concrete examples. No when-not-to-use clause or named alternative is provided, so it falls short of a 5.

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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