Skip to main content
Glama
78degrees
by 78degrees

scan_secrets

Find leaked API keys, tokens, and credentials across your machine. Scans .env files, configs, and shell history, then provides a report with risk details and fix guidance.

Instructions

Scan your entire machine for leaked API keys, tokens, and credentials. Checks .env files, AWS/SSH/Docker/npm configs, shell history, and more. Returns a detailed report with risk explanations, rotation URLs, and fix guidance. Also returns structured JSON data (in a second content block) for visual rendering. If you have visualization capabilities, render the JSON as a visual dashboard with a health score gauge, color-coded finding cards, and auto-fix indicators. Everything runs locally -- no data leaves your machine.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
include_low_severityNoInclude low-severity findings (test keys, etc). Default: true.
Behavior4/5

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

With no annotations, the description carries full burden. It explicitly states local execution and no data leaving the machine, and implies non-destructive reading. However, it does not mention performance impact or error handling.

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?

The description is front-loaded with the core purpose and scope. It is moderately concise but includes some extraneous visual rendering instructions. Every sentence adds value, though slightly verbose.

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?

Given simple schema (1 optional param), no output schema, and no annotations, the description adequately covers purpose, return content, and privacy. Missing details on limitations or edge cases, but sufficient for a scan tool.

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?

The only parameter (include_low_severity) is fully described in the schema (100% coverage). The description adds no further detail beyond mentioning low-severity findings in the report context, 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?

The description clearly states the verb (scan) and resource (entire machine for secrets). It distinguishes from siblings like fix_secrets (fixing) and scan_summary (summarizing) by detailing the scanning scope and output.

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 for initial secret detection but lacks explicit guidance on when to use vs alternatives, such as preferring scan_summary for aggregated results or fix_secrets for remediation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/78degrees/ghosthunt'

If you have feedback or need assistance with the MCP directory API, please join our Discord server