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Kirachon

Context Engine MCP Server

by Kirachon

Scrub Secrets

scrub_secrets
Read-onlyIdempotent

Detect and mask API keys, tokens, and passwords in content before sending to language models to prevent secret leakage.

Instructions

Scrub secrets from content before sending to LLM.

Detects and masks 15+ types of secrets:

  • AWS keys, OpenAI/Anthropic API keys

  • GitHub tokens, Stripe keys, Firebase/Supabase keys

  • Private keys (PEM), JWTs, connection strings

  • Generic API keys and passwords

Use this before including user content in prompts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesContent to scrub secrets from
show_endNoCharacters to show at end of masked secret (default: 0)
show_startNoCharacters to show at start of masked secret (default: 4)
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, indicating no side effects. The description adds value by detailing the types of secrets detected and masked, enhancing transparency 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 and well-structured: a primary sentence, a bulleted list of secret types, and a usage instruction. It is front-loaded with the core purpose, with no wasted sentences.

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 3 parameters (1 required), high schema coverage, and annotations, the description is sufficiently complete. It explains purpose, usage, and secret types, although it does not describe return values (acceptable without output schema).

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%, so the schema already documents all parameters. The description lists secret types but does not add parameter-specific semantics beyond the schema, meeting the baseline.

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 tool's purpose: 'Scrub secrets from content before sending to LLM.' It lists specific secret types, distinguishing it from sibling tools like add_memory or clear_index which have no secret-scrubbing functionality.

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?

The description explicitly recommends using this tool 'before including user content in prompts,' providing clear usage guidance. It does not mention alternatives or exclusions, but the context is sufficient for this purpose.

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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