API-mega-list vs IntelliCode
Side-by-side comparison to help you choose.
| Feature | API-mega-list | IntelliCode |
|---|---|---|
| Type | MCP Server | Extension |
| UnfragileRank | 39/100 | 40/100 |
| Adoption | 0 | 1 |
| Quality | 0 | 0 |
| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 14 decomposed | 7 decomposed |
| Times Matched | 0 | 0 |
Fetches actor metadata from Apify's platform API via paginated requests (fetch_apify_actors.js), processes ~10,577 raw actors, filters out 79 test/placeholder entries, and stores normalized JSON in apify_actors.json. The system runs on a daily schedule to maintain currency without manual intervention, using direct API integration rather than web scraping the Apify platform itself.
Unique: Uses direct Apify platform API integration with pagination rather than web scraping, enabling reliable daily refresh of 10,498 production APIs with automated filtering of test actors — a rare approach for API directories that typically rely on manual curation or scraping.
vs alternatives: More maintainable than web-scraping-based API directories because it uses official Apify APIs, ensuring compatibility and reducing brittleness compared to regex-based HTML parsing approaches used by generic awesome-lists.
Transforms normalized JSON actor data into a hierarchical markdown documentation structure using generate_readme_clean.js. Generates a main README.md (10,498 entries) plus 18 category-specific subdirectories, each with its own README containing filtered API listings. Uses consistent markdown formatting and table-of-contents generation to enable both top-level browsing and deep category exploration.
Unique: Generates both a monolithic main README (10,498 entries) AND 18 category-specific READMEs from a single JSON source, enabling both comprehensive discovery and focused category browsing — most API directories choose one approach (either flat listing or category-only).
vs alternatives: Provides better GitHub UX than flat API lists (easier to navigate categories) while maintaining a complete reference document, whereas alternatives like Postman Collections or Swagger hubs require external tools to browse and don't integrate with GitHub's native markdown rendering.
Includes 2,652 Developer Tools APIs (25% of catalog) covering integrations, open source APIs, and infrastructure services. These APIs enable developers to extend Apify's capabilities, integrate with external systems (webhooks, databases, message queues), and build custom automation workflows using open source components.
Unique: Dedicates 2,652 APIs (25% of catalog) to developer tools and integrations, recognizing that extensibility is critical for enterprise adoption — most API directories do not explicitly surface integration and infrastructure APIs.
vs alternatives: Enables developers to build custom Apify workflows with external systems, whereas generic API directories require manual integration research.
Aggregates APIs for extracting content and media (news articles, blog posts, videos), news data (headlines, sources, sentiment), and employment data (job listings, salary information, company data) across 4 dedicated categories. These APIs enable content aggregation, news monitoring, job market analysis, and employment research without relying on official platform APIs.
Unique: Dedicates 4 separate categories (Content & Media, News, Jobs, Travel) to domain-specific data extraction, recognizing that content, news, and employment are distinct use cases — most API directories combine these under generic 'data extraction' categories.
vs alternatives: Provides specialized APIs for content and employment data extraction, whereas generic API directories require keyword search to find relevant tools.
Includes Travel APIs and Business APIs for extracting travel data (flights, hotels, reviews), business information (company data, financial information, market intelligence), and commerce data. These APIs enable travel price monitoring, business research, and market intelligence without relying on official platform APIs.
Unique: Includes dedicated Travel and Business categories reflecting Apify's strength in travel and commerce data extraction — most API directories do not specialize in travel data scraping.
vs alternatives: Provides specialized travel and business data extraction APIs, whereas generic API directories require keyword search to find relevant tools.
Includes SEO Tools APIs for extracting search engine data, keyword rankings, backlink information, and SEO metrics. These APIs enable SEO monitoring, competitor analysis, and search optimization without relying on official search engine APIs.
Unique: Includes dedicated SEO Tools category recognizing the importance of search optimization for digital marketing — most API directories do not specialize in SEO data extraction.
vs alternatives: Provides specialized SEO scraping APIs, whereas generic API directories require keyword search to find SEO tools.
Organizes 10,498 APIs into 18 functional categories (Automation, Lead Generation, Social Media, Developer Tools, E-commerce, AI & Intelligence, Real Estate, SEO Tools, Business, Content & Media, News, Jobs, Travel, Integrations, Open Source, MCP Servers, and Others) with each category containing a filtered README and direct links to Apify execution pages. Enables users to navigate by use case rather than platform, with category distribution showing Automation (46%), Lead Generation (33%), and Social Media (31%) as dominant categories.
Unique: Uses functional use-case categories (Automation, Lead Generation, Real Estate) rather than technical categories (REST, GraphQL, Webhooks) or platform categories (Twitter, LinkedIn, Amazon), making it accessible to non-technical users while maintaining technical precision for developers.
vs alternatives: More intuitive than RapidAPI or ProgrammableWeb which organize by API provider, and more comprehensive than vertical-specific directories because it covers 18 domains in a single unified catalog with consistent metadata.
Each API entry in the documentation includes a direct hyperlink to the actor's execution page on apify.com (format: apify.com/actors/{actor-id}), enabling users to launch the API without leaving the GitHub documentation. This integration pattern bypasses the need for API key management or local setup — users click a link and execute the actor directly on Apify's infrastructure with a web UI.
Unique: Provides direct hyperlinks to Apify's web UI execution pages rather than requiring users to copy actor IDs or manage API credentials, creating a frictionless discovery-to-execution flow that treats the GitHub catalog as a launchpad rather than just documentation.
vs alternatives: More accessible than API directories that require REST API integration (RapidAPI, ProgrammableWeb) because it enables no-code execution, while maintaining the ability to integrate programmatically for advanced users.
+6 more capabilities
Provides IntelliSense completions ranked by a machine learning model trained on patterns from thousands of open-source repositories. The model learns which completions are most contextually relevant based on code patterns, variable names, and surrounding context, surfacing the most probable next token with a star indicator in the VS Code completion menu. This differs from simple frequency-based ranking by incorporating semantic understanding of code context.
Unique: Uses a neural model trained on open-source repository patterns to rank completions by likelihood rather than simple frequency or alphabetical ordering; the star indicator explicitly surfaces the top recommendation, making it discoverable without scrolling
vs alternatives: Faster than Copilot for single-token completions because it leverages lightweight ranking rather than full generative inference, and more transparent than generic IntelliSense because starred recommendations are explicitly marked
Ingests and learns from patterns across thousands of open-source repositories across Python, TypeScript, JavaScript, and Java to build a statistical model of common code patterns, API usage, and naming conventions. This model is baked into the extension and used to contextualize all completion suggestions. The learning happens offline during model training; the extension itself consumes the pre-trained model without further learning from user code.
Unique: Explicitly trained on thousands of public repositories to extract statistical patterns of idiomatic code; this training is transparent (Microsoft publishes which repos are included) and the model is frozen at extension release time, ensuring reproducibility and auditability
vs alternatives: More transparent than proprietary models because training data sources are disclosed; more focused on pattern matching than Copilot, which generates novel code, making it lighter-weight and faster for completion ranking
IntelliCode scores higher at 40/100 vs API-mega-list at 39/100. API-mega-list leads on quality and ecosystem, while IntelliCode is stronger on adoption.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Analyzes the immediate code context (variable names, function signatures, imported modules, class scope) to rank completions contextually rather than globally. The model considers what symbols are in scope, what types are expected, and what the surrounding code is doing to adjust the ranking of suggestions. This is implemented by passing a window of surrounding code (typically 50-200 tokens) to the inference model along with the completion request.
Unique: Incorporates local code context (variable names, types, scope) into the ranking model rather than treating each completion request in isolation; this is done by passing a fixed-size context window to the neural model, enabling scope-aware ranking without full semantic analysis
vs alternatives: More accurate than frequency-based ranking because it considers what's in scope; lighter-weight than full type inference because it uses syntactic context and learned patterns rather than building a complete type graph
Integrates ranked completions directly into VS Code's native IntelliSense menu by adding a star (★) indicator next to the top-ranked suggestion. This is implemented as a custom completion item provider that hooks into VS Code's CompletionItemProvider API, allowing IntelliCode to inject its ranked suggestions alongside built-in language server completions. The star is a visual affordance that makes the recommendation discoverable without requiring the user to change their completion workflow.
Unique: Uses VS Code's CompletionItemProvider API to inject ranked suggestions directly into the native IntelliSense menu with a star indicator, avoiding the need for a separate UI panel or modal and keeping the completion workflow unchanged
vs alternatives: More seamless than Copilot's separate suggestion panel because it integrates into the existing IntelliSense menu; more discoverable than silent ranking because the star makes the recommendation explicit
Maintains separate, language-specific neural models trained on repositories in each supported language (Python, TypeScript, JavaScript, Java). Each model is optimized for the syntax, idioms, and common patterns of its language. The extension detects the file language and routes completion requests to the appropriate model. This allows for more accurate recommendations than a single multi-language model because each model learns language-specific patterns.
Unique: Trains and deploys separate neural models per language rather than a single multi-language model, allowing each model to specialize in language-specific syntax, idioms, and conventions; this is more complex to maintain but produces more accurate recommendations than a generalist approach
vs alternatives: More accurate than single-model approaches like Copilot's base model because each language model is optimized for its domain; more maintainable than rule-based systems because patterns are learned rather than hand-coded
Executes the completion ranking model on Microsoft's servers rather than locally on the user's machine. When a completion request is triggered, the extension sends the code context and cursor position to Microsoft's inference service, which runs the model and returns ranked suggestions. This approach allows for larger, more sophisticated models than would be practical to ship with the extension, and enables model updates without requiring users to download new extension versions.
Unique: Offloads model inference to Microsoft's cloud infrastructure rather than running locally, enabling larger models and automatic updates but requiring internet connectivity and accepting privacy tradeoffs of sending code context to external servers
vs alternatives: More sophisticated models than local approaches because server-side inference can use larger, slower models; more convenient than self-hosted solutions because no infrastructure setup is required, but less private than local-only alternatives
Learns and recommends common API and library usage patterns from open-source repositories. When a developer starts typing a method call or API usage, the model ranks suggestions based on how that API is typically used in the training data. For example, if a developer types `requests.get(`, the model will rank common parameters like `url=` and `timeout=` based on frequency in the training corpus. This is implemented by training the model on API call sequences and parameter patterns extracted from the training repositories.
Unique: Extracts and learns API usage patterns (parameter names, method chains, common argument values) from open-source repositories, allowing the model to recommend not just what methods exist but how they are typically used in practice
vs alternatives: More practical than static documentation because it shows real-world usage patterns; more accurate than generic completion because it ranks by actual usage frequency in the training data