AI Distiller MCP vs Privacyscrubber MCP
A side-by-side look at the AI Distiller MCP and Privacyscrubber MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose AI Distiller MCP for security work as a local process; choose Privacyscrubber MCP for security as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose AI Distiller MCP when
- You need dedicated capabilities in the Security domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: No auth required.
- You want tools like
distill_file, distill_directory, distill_with_dependencies.
Choose Privacyscrubber MCP when
- You need dedicated capabilities in the Security domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You want tools like
audit_context, sanitize_text, scrub_text.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Security | Security |
| Quality signal | 65/100 · Great | 65/100 · Great |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | No auth required | API key required |
| Pricing | Free | Freemium |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 172 | 2 |
| Directory activity | 4 views 0 installs 0 upvotes | 5 views 0 installs 0 upvotes |
| Tools | 16 | 23 |
| Full listing | View AI Distiller MCP | View Privacyscrubber MCP |
Tools & capabilities
AI Distiller MCP tools 16
distill_fileExtracts essential code structure from a single file - the core functionality of AI Distiller. Returns clean, structured code signatures optimized for AI context windows. USAGE: Essential for providing accurate code context to AI assistants. Automatically detects language and extracts API signatures, types, and structure while removing unnecessary implementation details.distill_directoryExtracts essential code structure from entire directories - the core functionality of AI Distiller. Processes all supported programming languages and returns structured API/code signatures optimized for AI context windows. USAGE: Perfect for understanding codebases, API discovery, and providing accurate code context to AI assistants. Supports filtering by visibility levels, file patterns, and content types.distill_with_dependenciesAnalyzes call dependencies and returns distilled code of only relevant methods/classes up to specified depth. This advanced feature traces function/method calls across files and includes only the code that is actually called from the target file, creating focused distillations for deep code analysis. USAGE: Perfect for understanding code execution flows, impact analysis, and creating focused context for AI assistants. Particularly useful for large codebases where you need to understand how specific functionality works across multiple files.aid_hunt_bugsGenerates a bug hunting prompt with distilled code for AI agents to systematically identify potential bugs, logical errors, race conditions, and quality issues. Use when you need AI to analyze code for hidden bugs or perform a comprehensive code health check. OUTPUT: Generates a markdown file with bug hunting prompt and distilled code. The response includes the file path - AI agents should read this file and follow instructions to perform the actual bug analysis.aid_suggest_refactoringGenerates a refactoring analysis prompt with distilled code for AI agents to identify specific refactoring opportunities. Use when you need AI to suggest improvements for code quality, readability, maintainability, or performance. OUTPUT: Generates a markdown file with refactoring prompt and distilled code. The response includes the file path - AI agents should read this file and follow instructions to provide refactoring suggestions with before/after examples.aid_generate_diagramGenerates a diagram creation prompt with distilled code for AI agents to create architectural diagrams in Mermaid format. Use when you need AI to generate flowcharts, sequence diagrams, class diagrams, and architecture overviews. OUTPUT: Generates a markdown file with diagram generation prompt and distilled code. The response includes the file path - AI agents should read this file and follow instructions to create 10+ Mermaid diagrams.aid_analyze_securityGenerates a security analysis prompt with distilled code for AI agents to perform comprehensive security audits with OWASP Top 10 focus. Use when you need AI to identify vulnerabilities, security anti-patterns, and weak points. OUTPUT: Generates a markdown file with security audit prompt and distilled code. The response includes the file path - AI agents should read this file and follow instructions to analyze security vulnerabilities and suggest remediation.aid_generate_docsGenerates documentation creation prompts with distilled code for AI agents to create comprehensive documentation including API references, usage examples, and developer guides. Use when you need AI to generate technical documentation from code. OUTPUT: Generates markdown files with documentation prompts and distilled code. The response includes file paths - AI agents should read these files and follow instructions to create the actual documentation.aid_deep_file_analysisGenerates task lists and prompts for systematic file-by-file analysis. Creates a structured workflow where AI agents analyze each file across multiple dimensions (Security, Performance, Maintainability, Readability). Perfect for comprehensive codebase reviews. OUTPUT: Generates task list, summary template, and directory structure for organizing analysis results. AI agents should read the task list and follow instructions systematically.aid_multi_file_docsCreates documentation workflow prompts with file relationships. Generates structured prompts that guide AI agents to create interconnected documentation covering multiple files, their relationships, and overall system architecture. OUTPUT: Generates workflow files that AI agents can follow to create comprehensive documentation with proper cross-references.aid_complex_analysisEnterprise-grade analysis prompt with full codebase context. Generates comprehensive prompts for architecture analysis, compliance checks, and detailed findings. Best suited for large codebases requiring deep architectural insights. OUTPUT: Generates analysis prompt with distilled code that AI agents can use to create architecture diagrams, identify patterns, and provide strategic recommendations.aid_performance_analysisPerformance optimization prompt with complexity focus. Generates analysis prompts that guide AI agents to identify performance bottlenecks, analyze algorithmic complexity, and suggest optimization strategies. OUTPUT: Generates performance analysis prompt focusing on scalability issues, resource usage, and optimization opportunities.- +4 more on the listing page
Privacyscrubber MCP tools 23
audit_contextSTEP 0 (Pre-Flight): Non-destructive security inspection of raw prompts or document chunks before sending to LLMs. Evaluates PII, secrets, risk severity (CLEAN, LOW, MODERATE, CRITICAL), and triggered regulatory frameworks (GDPR, HIPAA, SOC 2, PCI DSS) with zero text mutation.sanitize_textSTEP 1: Call this first. You MUST NOT process raw user data before calling this. Locally scrubs PII, secrets, and credentials (like API keys, passwords, emails, phones, names) from code, logs, or text. Replaces them with safe placeholders (e.g., [EMAIL_1], [API_KEY_1]). Keep your data secure before passing it to any LLM. (For in-code backend services or RAG vector pipelines outside of MCP, use '@privacyscrubber/sdk': npm i @privacyscrubber/sdk)scrub_textAlias for 'sanitize_text'. Locally scrubs PII and secrets before LLM ingestion.reveal_textSTEP 3: Call this last. You MUST pass your final generated response through this tool to restore tokens (e.g., [EMAIL_1]) back with the original private data from the local volatile RAM-only session map before showing it to the user.sanitize_fileReads a local file, sanitizes its contents using the selected profile, and outputs the safe version for AI analysis. Securely keeps original identifiers in memory.scrub_fileAlias for 'sanitize_file'. Reads and sanitizes a local file.audit_directory_for_piiScans a local directory for leaks of secrets, keys, and PII. Returns a summary report. Use this tool for Security Auditing.redact_fileAction/Redact: In-place redaction of a local file. Replaces PII and secrets with tokens and saves the file. By default, creates a .bak backup. Use dry_run=true to test without modifying.create_default_configCreates a default 'privacyscrubber.json' configuration file in the active workspace root directory if one does not exist. Includes template structures for custom regex rules and exclusion bypass patterns.generate_compliance_reportGenerates an official Zero-Trust Compliance Audit Certificate (GDPR, HIPAA, EU AI Act, SOC 2) for the current MCP session. Returns cryptographic session hash, masked entities breakdown, and compliance certification.mark_false_positiveMarks a previously detected token as a false positive. The original plaintext value will be excluded from all future sanitize_text calls in this session. Returns the restored original value and updated ignore list size.check_statusReturns the current PrivacyScrubber MCP tier, session usage, available profiles, and PRO upgrade instructions. Call this to see your license status or get setup help.- +11 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
AI Distiller MCP
{
"mcpServers": {
"janreges-ai-distiller-mcp": {
"command": "npx",
"args": [
"-y",
"@janreges/ai-distiller-mcp"
]
}
}
}
Privacyscrubber MCP
{
"mcpServers": {
"moxno-privacyscrubber-mcp": {
"command": "npx",
"args": [
"-y",
"@privacyscrubber/mcp-server"
]
}
}
}
Frequently asked questions
AI Distiller MCP is categorized under Security and uses a local stdio subprocess. Privacyscrubber MCP belongs to Security and uses a local stdio subprocess. Pick AI Distiller MCP for security work and Privacyscrubber MCP for security.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
AI Distiller MCP is listed as free. Auth: no auth required. Privacyscrubber MCP is listed as freemium — a free tier with paid plans. Auth: api key required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add janreges-ai-distiller-mcp and moxno-privacyscrubber-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, AI Distiller MCP has a quality signal of 65/100 (Great) with 4 views, 0 installs and 172 GitHub stars. Privacyscrubber MCP has 65/100 (Great) with 5 views, 0 installs and 2 GitHub stars.