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rshade
by rshade

dotenv_environment

Load and expose environment variables from .env files to AI assistants. Automatically mask sensitive values like passwords and API keys for safe configuration access.

Instructions

Load and parse environment variables from .env files, making them visible to AI assistants through MCP context. Automatically masks sensitive values (passwords, tokens, API keys) while exposing configuration values safely.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileNoPath to .env file (default: .env)
maskNoMask sensitive values (default: true)
directoryNoWorking directory (default: current directory)
maskPatternsNoCustom patterns to mask (in addition to defaults: PASSWORD, SECRET, TOKEN, KEY, API_KEY, PRIVATE, CREDENTIALS, AUTH)
includeProcessEnvNoInclude process.env variables (default: false)
Behavior3/5

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

With no annotations, the description carries full burden. It discloses automatic masking of sensitive values and that variables become visible to AI assistants. However, it lacks details on side effects (e.g., override behavior, persistence, error handling) and omits information about return values or state changes.

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?

The description is only two sentences, front-loads the core purpose, and contains no redundant information. Every sentence adds value.

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 tool with 5 parameters, no output schema, and no annotations, the description adequately covers the main purpose and the key behavioral aspect (masking). However, it does not explain what happens after loading (e.g., return type, side effects like overriding existing env vars), leaving some gaps for an AI agent to infer.

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 coverage is 100% with all 5 parameters described in the schema. The description adds no additional parameter context beyond what the schema provides. Baseline score of 3 is appropriate.

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?

The description uses specific verbs ('Load and parse') and clearly identifies the resource ('.env files'). It also distinguishes the tool's purpose by mentioning automatic masking of sensitive values, which is unique among many sibling tools.

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?

The description implies usage when environment variables from .env files need to be loaded, but it does not explicitly state when to use this tool versus alternatives or when not to use it. No usage exclusions or alternative recommendations are provided.

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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