In-depth architectural comparison of the Mcpskills Server and AI Distiller MCP MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Mcpskills Server
Security · Local stdio
Quality: 59/100 (Good) | Auth: API Key required
AI Distiller MCP
Security · Local stdio
Quality: 65/100 (Great) | Auth: No auth required
Verdict Summary: Choose Mcpskills Server if you need specialized Security tools running via a local process. Choose AI Distiller MCP if your workspace requires Security integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Mcpskills Server when:
You need dedicated capabilities in the Security domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: API Key required (Freemium).
You have access to required keys: MCPSKILLS_API_KEY.
Pre-install trust layer for MCP servers, AI skills, and npm packages. Scores any repo or package across 15 signals (incl. OSV/KEV/EPSS vulnerability intelligence) with safety scanning for prompt injection, credential theft, and supply-chain risk; the autogate tool returns a go/no-go install decision. Listed in the official MCP Registry as io.mcpskills/server. npm: @mcpskillsio/server. https://mcpskills.io
Extracts essential code structure from large codebases into AI-digestible format, helping AI agents write code that correctly uses existing APIs on the first attempt.
Category & Scope
Tools & Capabilities Breakdown
Mcpskills Server Tools (9)
check_trust_score
Score any AI skill, MCP server, or GitHub repo for trustworthiness. Returns a trust score (0-10) across 4 dimensions: Alive, Legit, Solid, Usable. Accepts: owner/repo, GitHub URL, npm package (npm:@scope/name or @scope/name), Smithery URL, or OpenClaw URL. AI skills get enhanced safety scanning. Set MCPSKILLS_API_KEY for full reports.
scan_safety
Run a focused safety scan on an AI skill or MCP server. Checks for prompt injection, shell execution, network exfiltration, credential theft, obfuscated payloads, public network binding (0.0.0.0), and risky npm lifecycle scripts (preinstall/install/postinstall). Accepts any input format (owner/repo, npm package, Smithery URL, etc.).
list_packages
Browse curated, pre-scored AI skill packages organized by use case. Each package contains vetted skills with trust scores. Available packages: Claude Power User, Full-Stack Vibe Coder, Data & Research, DevOps & Infrastructure, Content & Marketing.
get_badge
Get a trust badge URL for any repo or package. Returns a shields.io-style SVG badge showing the trust score and tier. Embed in READMEs. Badge auto-updates hourly.
Ready-to-Paste Client Configurations
Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).
Mcpskills Server is categorized under Security and uses a local stdio subprocess. In contrast, AI Distiller MCP belongs to Security using local stdio subprocess. Select Mcpskills Server when you need capabilities focused on security and AI Distiller MCP when you require tools for security.
Start monitoring a repo or package for trust score changes. Alerts when score changes significantly (±0.3 points or tier change). Requires a paid API key.
check_watched
Re-scan all watched repos and check for score changes. Returns any repos whose trust score changed significantly since last check.
batch_check
Check up to 5 repos or packages in one call. Returns a trust assessment for each. Requires a Developer Pro or Team API key. Accepts any mix of formats (owner/repo, npm packages, registry URLs).
auto_gate
Should I install this? Returns a simple go/no-go decision with reasoning. Accepts any format: owner/repo, npm package, Smithery URL, etc. Returns { proceed: true/false, reason: "..." }.
build_stack
Recommend a vetted stack of trusted tools for a described task. Describe what you're building (e.g., "Next.js app with auth, payments, and AI chat") and get back a curated list of the highest-scoring repos in each relevant category, pre-scored and ready to install. Returns tool names, trust scores, tiers, and install hints. Use this instead of guessing which tools to recommend — every suggestion is backed by live trust data.
AI Distiller MCP Tools (16)
distill_file
Extracts 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_directory
Extracts 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_dependencies
Analyzes 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_bugs
Generates 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_refactoring
Generates 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_diagram
Generates 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_security
Generates 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_docs
Generates 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_analysis
Generates 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_docs
Creates 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_analysis
Enterprise-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_analysis
Performance 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.