AI Distiller MCP vs Reversecore MCP
A side-by-side look at the AI Distiller MCP and Reversecore 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 Reversecore 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 Reversecore MCP when
- You need dedicated capabilities in the Security domain.
- You prefer a local stdio subprocess transport.
- You want tools like
audit_source_code, run_file, copy_to_workspace.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Security | Security |
| Quality signal | 65/100 · Great | 63/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | No auth required | Not declared |
| Pricing | Free | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 172 | 212 |
| Directory activity | 4 views 0 installs 0 upvotes | 2 views 0 installs 0 upvotes |
| Tools | 16 | 114 |
| Full listing | View AI Distiller MCP | View Reversecore 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
Reversecore MCP tools 114
audit_source_codePerform a security audit on a source code file (SAST). This tool analyzes source code (C, C++, Python, Go, etc.) to identify potential security vulnerabilities such as buffer overflows, injection flaws, and weak cryptography. It provides a structured breakdown of dangerous patterns found in the code to guide deeper manual or AI-driven analysis. Use this tool when you have access to original source code or when you want to analyze decompiled output exported to a file.run_fileIdentify file metadata using the ``file`` CLI utility.copy_to_workspaceCopy any accessible file to the workspace directory. This tool allows copying files from any location (including AI agent upload directories) to the workspace where other reverse engineering tools can access them. Supports files from: - Claude Desktop uploads (/mnt/user-data/uploads) - Cursor uploads - Windsurf uploads - Local file paths - Any other accessible locationcreate_directoryCreate a new sub-directory within the workspace. This tool allows AI agents to dynamically create isolated sub-workspaces for different tasks or sessions.list_workspaceList all files in the workspace directory. Returns: ToolResult with list of files in workspacescan_workspaceBatch scan all files in the workspace using multiple tools in parallel. This tool performs a comprehensive scan of the workspace to identify files, analyze binaries, and detect threats. It runs 'run_file', 'parse_binary_with_lief', and 'run_yara' (if rules exist) on all matching files concurrently. **Workflow:** 1. Identify files matching patterns (default: all files) 2. Run 'file' command on all files 3. Run 'LIEF' analysis on executable files 4. Run 'YARA' scan if rules are available 5. Aggregate results into a single reportexplain_patchAnalyze differences between two binaries and explain changes in natural language. This tool combines binary diffing with decompilation to help understand security patches. It identifies changed functions, decompiles them, and uses heuristics to explain the nature of the changes (e.g., "Added bounds check", "Replaced unsafe API").assemble_instructionsAssemble assembly instruction strings into raw machine byte values using Keystone.create_memory_sessionCreate a new memory session to store memories. Use this when starting a new reverse engineering analysis. The session name should be descriptive and follow a template format like 'malware_analysis_2024_001' or 'game_cheat_detection'.save_memory_itemSave important information to long-term memory. Use this to remember: - Function addresses and their purposes - Vulnerability patterns discovered - API call sequences - User instructions and preferences - Interesting strings or structuresrecall_memory_itemSearch and recall memories from past analyses. Use this when you need to remember something from earlier, or when the user asks about previous findings.list_memory_sessionsList all analysis sessions with timestamps and status. Use this to see what analyses have been done before, or to find a session to resume.- +102 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"
]
}
}
}
Reversecore MCP
{
"mcpServers": {
"reversecore-mcp": {
"command": "uvx",
"args": [
"reversecore-mcp"
]
}
}
}
Frequently asked questions
AI Distiller MCP is categorized under Security and uses a local stdio subprocess. Reversecore MCP belongs to Security and uses a local stdio subprocess. Pick AI Distiller MCP for security work and Reversecore 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. Reversecore MCP has no declared pricing — check the project's own site. 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 reversecore-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. Reversecore MCP has 63/100 (Good) with 2 views, 0 installs and 212 GitHub stars.