Aeo MCP vs AI Distiller MCP — MCP Server Comparison | AllMCPs
Aeo MCP vs AI Distiller MCP
A side-by-side look at the Aeo MCP and AI Distiller MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Aeo MCP for security work as a local process; choose AI Distiller 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 Aeo MCP when
You need dedicated capabilities in the Security domain.
You prefer a local stdio subprocess transport.
You want tools like audit_url, check_ai_crawlers, inspect_llms_txt.
audit_urlRun a full AI-readiness (AEO) audit of a website: 29 checks across AI crawler access, structured data, content extractability, and answerability, returning a 0-100 score with prioritized fixes. Use when asked how visible a site is to ChatGPT, Perplexity, Gemini, or Claude, or for an overall AEO/AI-SEO health check.
check_ai_crawlersCheck whether AI crawlers like GPTBot, ClaudeBot, PerplexityBot, Google-Extended, CCBot, and Bingbot are allowed to read a website, based on its robots.txt. Use when asked about AI visibility, AEO, crawler permissions, or why a site does not appear in ChatGPT or Perplexity.
inspect_llms_txtFetch and validate a website's llms.txt file (the Markdown file that tells AI engines what the site is about and which pages matter). Returns its title, summary, sections, links, and any structural issues. Use when asked whether a site has llms.txt or how to improve it.
extract_structured_dataExtract and summarize a page's JSON-LD structured data (schema.org): which types exist (Product, Article, FAQPage, Organization), their key fields, and any parse errors. Use when asked whether a page has the schema markup AI answer engines rely on for citations.
extract_page_signalsExtract the on-page signals AI engines parse: title, meta description, h1, canonical, lang, text-to-HTML ratio, heading outline, and whether the page looks client-side rendered (invisible to most AI crawlers). Use to diagnose why engines misread or skip a page.
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.
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.
Aeo MCP is categorized under Security and uses a local stdio subprocess. AI Distiller MCP belongs to Security and uses a local stdio subprocess. Pick Aeo MCP for security work and AI Distiller 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.
Aeo MCP has no declared pricing — check the project's own site. AI Distiller MCP is listed as free. Auth: no auth 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 aeo-mcp and janreges-ai-distiller-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Aeo MCP has a quality signal of 53/100 (Good) with 2 views, 0 installs and 0 GitHub stars. AI Distiller MCP has 65/100 (Great) with 4 views, 0 installs and 172 GitHub stars.
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.