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Check Secrets

check_secrets
Read-onlyIdempotent

Scan source code for hardcoded secrets — cloud provider keys, API tokens, connection strings, private keys, and passwords — before they are committed. Supports Python, JavaScript, Java, Go, and more.

Instructions

Scan source code (or snippet) for hardcoded secrets — cloud provider keys, API tokens, connection strings, private keys, passwords. Supports Python, JavaScript, TypeScript, Java, Go, Ruby, Shell, Bash. Use to detect leaked credentials before commit; for injection detection use check_injection. Free: 30/hr, Pro: 500/hr. Returns {total, by_severity, findings}. No data stored. The generic password-assignment rule is suppressed when a more-specific credential rule fires on the same line — one targeted finding per leaked secret, not two.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesSource code string to scan for secrets (can be a single file or code snippet)
languageNoProgramming language of the code. Must be one of: python, javascript, typescript, java, go, ruby, shell, bash, generic. Use 'generic' if unsure.generic

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds 'No data stored' and explains the suppression rule for generic password-assignment, providing context beyond annotations.

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 concise (5 sentences), front-loaded with the main action, and includes necessary details without fluff.

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

Completeness5/5

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

With an output schema provided, the description doesn't need to explain return values. It covers purpose, usage, behavioral traits, and parameters sufficiently.

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 detailed descriptions for both parameters. The description lists supported languages but does not add significant new meaning beyond the schema. Baseline 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 explicitly states 'Scan source code (or snippet) for hardcoded secrets' and lists types of secrets (cloud provider keys, API tokens, etc.), distinguishing it from the sibling 'check_injection' for injection detection.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides clear when-to-use guidance: 'Use to detect leaked credentials before commit' and contrasts with an alternative: 'for injection detection use check_injection'. Rate limits (30/hr free, 500/hr Pro) are also included.

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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