Depscope vs AI Distiller MCP — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
Depscope vs AI Distiller MCP
In-depth architectural comparison of the Depscope 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
Depscope
Security · Local stdio
Quality: 60/100 (Good) | Auth: No auth required
AI Distiller MCP
Security · Local stdio
Quality: 65/100 (Great) | Auth: No auth required
Verdict Summary: Choose Depscope 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 Depscope when:
You need dedicated capabilities in the Security domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Package Intelligence for AI agents. 22 tools across 17 ecosystems (npm/pypi/cargo/go/maven/nuget/rubygems/composer/pub/hex/swift/cocoapods/cpan/hackage/cran/conda/homebrew) — check health, vulnerabilities (OSV + CISA KEV + EPSS), typosquats, malicious flags, alternatives, known bugs, breaking changes, stack compatibility and error-to-fix. 31k+ packages, 2.2k+ CVEs enriched. Zero auth, MIT. Remote URL https://mcp.depscope.dev/mcp or stdio npx depscope-mcp.
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
Depscope Tools (22)
check_malicious
Supply-chain malware check against OpenSSF/OSV. USE WHEN: about to suggest install of an unvetted/unfamiliar package; name came from a blog/tutorial. Call BEFORE check_package for untrusted pkgs. RETURNS: {is_malicious, threat_tier, source}.
check_typosquat
Typosquat detector. USE WHEN: name differs from a well-known package by 1-2 chars (`lodsh`, `reqeusts`); copy-paste from unreliable source; downloads near zero but name looks familiar. RETURNS: {is_typosquat, likely_target, confidence}.
check_bulk
Fast pre-flight filter for a batch of (ecosystem, package) pairs. DB-only, <100ms for 100 items. USE WHEN: about to emit `npm install a b c …` or `pip install a b c …` — catches hallucinated names, stdlib, typos, and known-bad in ONE call. NOT a dep-tree audit (use scan_project for that). RETURNS: per-item {status: exists|stdlib|malicious|typosquat_suspect|historical_incident|unknown}.
package_exists
Boolean registry existence check. USE WHEN: about to emit a package name in an install command but unsure it exists; verifying a name generated from training data. RETURNS: {exists}.
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).
Depscope is categorized under Security and uses a local stdio subprocess. In contrast, AI Distiller MCP belongs to Security using local stdio subprocess. Select Depscope when you need capabilities focused on security and AI Distiller MCP when you require tools for security.
Latest published version + deprecation flag — the cheapest call. USE WHEN: only a version string matters (pinning a dep, answering 'what version of X'). If you also need health/vulns use check_package. RETURNS: {latest, deprecated, published_at}.
get_health_score
Single 0-100 health score — cheapest go/no-go gate (>=70 safe). USE WHEN: CI gating or pkg already screened for malware/typos. NOT a first screen — run check_malicious + check_typosquat first. For a verbal verdict use get_package_prompt. RETURNS: {score, verdict}.
install_command
Canonical install command(s) across every package manager of the ecosystem (npm/pnpm/yarn/bun, pip/uv/poetry, cargo, go, composer, maven+gradle, nuget, …). USE WHEN: emitting an install line and you want correct flags. RETURNS: {primary, variants[]}.
get_package_prompt
LLM-optimised package brief — plain text ~300 tokens (~75% cheaper than JSON). Verdict (SAFE/AVOID/URGENT/MALICIOUS) + health + vulns + alternatives + maintainer alerts. USE WHEN: you want to reason over a package and drop the output directly in context; 'is X safe'. PREFER THIS over check_package in 95% of LLM cases. RETURNS: plain-text brief.
check_package
Full machine-readable JSON report (~2k tokens). USE WHEN: you need to programmatically parse specific fields (CI gating, UI, sub-field extraction). Otherwise prefer get_package_prompt. RETURNS: {package, health:{score}, vulnerabilities[], latest, deprecated, maintainers, recommendation}.
get_vulnerabilities
CVE/OSV advisories affecting the latest (or specified) version. USE WHEN: security-sensitive project; user asks 'any CVEs in X'; you already know the pkg exists. RETURNS: {vulnerability_count, vulnerabilities[]: {id, severity, cvss, fixed_in}}.
find_alternatives
Curated replacements for deprecated/unhealthy packages, including stdlib built-ins (e.g. `fs.rm` for rimraf). USE WHEN: pkg flagged AVOID/URGENT; 'what to use instead of X'; before guessing a replacement name. RETURNS: {alternatives[]: {name, reason, is_stdlib}}.
get_migration_path
Prescriptive migration plan between DIFFERENT packages — rationale + literal code diff + breaking changes + effort minutes. USE WHEN: replacing `request`→`axios`, `moment`→`dayjs`, `flask`→`fastapi`, etc.; both endpoints known. RETURNS: {rationale, diff, breaking_changes[], estimated_minutes}.
+10 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.