In-depth architectural comparison of the Bright Security 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
Bright Security
Security · Remote HTTP/SSE
Quality: 52/100 (Good) | Auth: No auth required
AI Distiller MCP
Security · Local stdio
Quality: 65/100 (Great) | Auth: No auth required
Verdict Summary: Choose Bright Security if you need specialized Security tools running via a hosted cloud SSE transport. 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 Bright Security when:
You need dedicated capabilities in the Security domain.
You prefer remote streaming HTTP/SSE transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
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.
List all projects accessible to your API key. Use this to find project IDs needed for other operations.
runDiscovery
Discover API endpoints using crawling (`crawlerUrls`) or API definitions (`fileId` from `uploadApiDefinition`). Before running, check if the project already has entrypoints with `listEntrypoints`. For private/local targets, specify a connected repeater via `repeaters`.
getDiscoveryStatus
Get the current status of a discovery run.
listDiscoveries
List discovery history for a project. View past discovery runs or monitor ongoing endpoint discovery.
uploadApiDefinition
Upload an API definition file (OpenAPI/Swagger) by URL or content. Returns a file ID to reference in discovery runs.
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).
Bright Security is categorized under Security and uses a remote streaming HTTP/SSE transport. In contrast, AI Distiller MCP belongs to Security using local stdio subprocess. Select Bright Security when you need capabilities focused on security and AI Distiller MCP when you require tools for security.
List discovered API endpoints/URLs for a project. Use this to select entrypoints for scans or evaluate attack surface coverage. Supports filtering by HTTP method, status, and text search.
getEntrypoint
Get detailed information about a specific entrypoint by ID, including headers, body, and configuration.
addEntrypoint
Add a new entrypoint to a project. Entrypoints define HTTP requests (method, URL, headers, body) that can be used for security scanning.
editEntrypoint
Update an existing entrypoint. Modify the HTTP request definition, authentication, or repeater settings.
runScan
Start a security scan against selected entrypoints. Supports targeting specific entrypoints by ID or by status (e.g., `new`, `changed`, `vulnerable`). Configure which tests to run or use a scan template. For private/local targets, specify a connected repeater.
getScanStatus
Get the current status of a running scan.
listScans
List scan history for a project. View past scan results or check the status of multiple ongoing scans.
+8 more tools listed on main page
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.
