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

by io.github.JcJamet
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env_parse

Read-onlyIdempotent

Parse a .env file content into a JSON object. Handles quoted values (single and double), inline comments, export prefix, and escaped sequences (\n, \t inside double quotes). Returns all key-value pairs. Use in CI/CD pipelines, agent config loaders, or when processing dotenv files programmatically.

Input Schema

NameRequiredDescriptionDefault
inputYes.env file content to parse (e.g. the output of `cat .env`)

Output Schema

NameRequiredDescriptionDefault
varsNo
countNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "input": "PORT=3000"
+  }
+]
  2. Changed1 schema field changed
    • addedOutput schema / properties
      Added value: +{
+  "count": {
+    "type": "number"
+  },
+  "vars": {}
+}
  3. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "type": "object"
+}
  4. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive/non-open-world, so the safety profile is covered. The description adds meaningful behavioral detail beyond that: quoted values, inline comments, export prefix, escape sequences, and that all key-value pairs are returned. It does not say how malformed lines or duplicate keys are handled, which is the remaining 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?

Three sentences, front-loaded with the core action and followed by supported syntax edge cases that genuinely earn their place. The closing usage sentence is the least essential but remains short and non-redundant.

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?

A one-parameter, read-only utility with an output schema and full annotation coverage needs little more than this; the description covers purpose, supported syntax, and output shape at a high level. Only edge-case/error behavior for malformed input is unaddressed.

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?

With one parameter at 100% schema description coverage, the schema already explains that the input is raw .env file content (with a `cat .env` example). The description confirms the input is whole file content rather than a path, but adds no format/size limits beyond what the schema says, so baseline 3 applies.

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 and resource ('Parse a .env file content into a JSON object') and immediately enumerates the syntax it handles, which pins down the exact transformation. This clearly separates it from format-conversion siblings like yaml_to_json or parse_csv without needing to name them.

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 usage contexts ('CI/CD pipelines, agent config loaders, or when processing dotenv files programmatically'), which tells an agent the intended scenario. It stops short of stating when NOT to use it or naming an alternative tool, but for a single-purpose formatter the surrounding context is unambiguous.

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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