| 0 |
| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 15 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Executes semantic search queries against Apple's official developer documentation API, returning ranked results with title, summary, and direct documentation links. Implements LRU caching with 10-minute TTL for search results (200 entry limit) to reduce redundant API calls while keeping results fresh for dynamic user queries. Integrates directly with Apple's search infrastructure rather than building a custom index, ensuring compatibility with the latest documentation updates.
Unique: Direct integration with Apple's official search API (not web scraping or custom indexing) combined with LRU caching strategy that balances freshness (10-min TTL) against API rate limits, enabling real-time documentation access within AI assistants without maintaining a separate search index
vs alternatives: Faster and more accurate than regex-based local search because it leverages Apple's own ranking algorithm, and more current than pre-built documentation snapshots because it queries live API with short cache windows
Fetches full documentation content for a specific Apple framework, class, or API by URL or identifier, parsing Apple's JSON API responses to extract structured content including method signatures, parameters, return types, and code examples. Implements 30-minute LRU cache (500 entries) for API documentation to optimize repeated lookups of the same framework while respecting Apple's documentation update cadence. Handles both Swift and Objective-C documentation formats transparently.
Unique: Parses Apple's native JSON documentation API (not HTML scraping) to extract structured metadata including parameter types, availability constraints, and code examples, with intelligent caching that respects the stability of API documentation (30-min TTL vs 10-min for search results)
vs alternatives: More reliable than web scraping because it uses official JSON APIs, and more comprehensive than static documentation snapshots because it includes real-time availability information and parameter metadata
Organizes WWDC video index by year (2014-2025) enabling developers to filter videos by specific WWDC events or year ranges. Supports queries like 'show me all WWDC 2023 sessions on SwiftUI' or 'find videos from the last 3 years about App Services'. Maintains historical context of how Apple's frameworks and best practices have evolved across WWDC events.
Unique: Organizes WWDC video index chronologically by year (2014-2025) with support for year-range filtering, enabling developers to understand framework evolution and best practices across multiple WWDC events
vs alternatives: More discoverable than Apple's WWDC website because filtering is integrated into AI assistants, and more contextual than YouTube playlists because year-based organization highlights framework evolution
Implements MCP server initialization, configuration loading, and graceful shutdown. Handles TypeScript compilation, environment variable loading, and MCP protocol handshake with clients (Claude Desktop, Cursor, VS Code). Manages server state including cache initialization and tool registry setup. Supports configuration via environment variables and config files.
Unique: Implements full MCP server lifecycle (initialization, configuration, tool registry setup, graceful shutdown) with support for multiple MCP clients (Claude Desktop, Cursor, VS Code, Windsurf, Zed, Cline) through standard MCP protocol
vs alternatives: More flexible than hardcoded MCP servers because it supports configuration-driven setup, and more robust than simple scripts because it handles protocol handshake and error recovery
Retrieves and caches method signatures, parameter types, return types, and availability information from Apple's documentation API. Enables AI assistants to understand the exact signature of an API before generating code that uses it. Validates parameter types and counts to catch potential errors early.
Unique: Parses Apple's JSON documentation API to extract structured method signatures with parameter types, return types, and availability constraints, enabling type-safe code generation without manual signature lookup
vs alternatives: More accurate than regex-based signature parsing because it uses official Apple metadata, and more comprehensive than static type stubs because it includes runtime availability information
Analyzes user queries to infer intent and recommend relevant documentation, frameworks, or WWDC videos. Uses keyword matching and topic correlation to suggest related documentation that may be useful. For example, a query about 'state management' might recommend SwiftUI documentation, Combine framework docs, and related WWDC sessions.
Unique: Infers user intent from natural language queries and recommends related documentation, frameworks, and WWDC videos based on topic correlation and keyword matching, rather than requiring explicit search parameters
vs alternatives: More helpful than simple search because it proactively suggests related content, and more discoverable than browsing documentation manually because recommendations are contextual to the user's current task
Supports querying multiple documentation items in a single request and aggregating results. Enables developers to retrieve documentation for multiple APIs, frameworks, or WWDC videos in parallel, reducing round-trip latency. Results are aggregated and deduplicated before returning to the client.
Unique: Supports batch documentation retrieval with parallel API calls and result aggregation, reducing latency for multi-item queries compared to sequential individual requests
vs alternatives: Faster than sequential requests because it parallelizes API calls, and more convenient than manual aggregation because results are deduplicated automatically
Searches a locally-maintained JSON index of 2,000+ WWDC videos (2014-2025) organized across 17 topic categories (SwiftUI, App Services, Developer Tools, Machine Learning, etc.) and chronologically by year. Implements instant local search without external API calls by maintaining an in-memory index of video metadata (title, description, year, topics, video ID). Supports multi-dimensional filtering: by topic (e.g., 'SwiftUI & UI Frameworks'), by year range, and by keyword matching against titles and descriptions.
Unique: Maintains a comprehensive local JSON index of WWDC videos organized into 17 specialized topic categories (SwiftUI, App Services, Developer Tools, Graphics & Games, Machine Learning, etc.) with year-based organization, enabling instant multi-dimensional filtering without external API calls or rate limits
vs alternatives: Faster and more reliable than web scraping Apple's WWDC site because it uses a pre-built local index, and more discoverable than YouTube search because results are curated by topic and platform relevance
+7 more capabilities
Generates single-line and multi-line code suggestions in real-time as developers type, using semantic indexing of the entire codebase to retrieve relevant type definitions, function signatures, and contextual patterns. The system maintains a 1M token context window (Pro/Team tiers) that enables suggestions informed by distant code definitions and cross-file dependencies, constructed via local codebase semantic search rather than simple token-based recency. Suggestions adapt to detected coding style on Pro/Team tiers through implicit pattern learning from recent edits.
Unique: 1M token context window with codebase-wide semantic indexing enables suggestions informed by distant code definitions and cross-file patterns, versus competitors (Copilot, Tabnine) that typically use fixed context windows (4K-32K tokens) or file-local context. Claimed 250ms latency suggests optimized retrieval pipeline, though indexing mechanism and performance at scale remain undisclosed.
vs alternatives: Larger context window than GitHub Copilot (8K-32K tokens) and faster latency than unnamed competitors (250ms vs 783ms claimed), enabling suggestions on large codebases with minimal typing delay; trade-off is cloud dependency and undisclosed free tier limitations.
Provides a separate chat interface supporting multiple LLM backends (GPT-4o, Claude 3.5 Sonnet, GPT-4, others) for conversational code assistance. Users attach files, reference recent edits, and trigger compiler diagnostic uploads; the system generates diffs and applies code changes directly to the editor. Model selection is per-conversation, and $5/month in credits (included in Pro/Team) covers external model API costs; overage pricing is undisclosed. Hotkey-driven workflow enables rapid context switching between inline completion and chat.
Unique: Multi-model chat interface with per-conversation model selection and integrated diff application, combined with compiler diagnostic auto-upload. Unlike Copilot Chat (single model per tier) or standalone ChatGPT, Supermaven Chat unifies multiple LLM backends in a single hotkey-driven workflow with direct editor integration for change application.
Supermaven scores higher at 71/100 vs apple-docs-mcp at 39/100. apple-docs-mcp leads on ecosystem, while Supermaven is stronger on adoption and quality.
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vs alternatives: Supports multiple LLM backends (GPT-4o, Claude 3.5 Sonnet) in one interface with included credits, whereas GitHub Copilot Chat is single-model per tier and requires separate ChatGPT subscription for model switching; trade-off is credit limits and unknown overage pricing.
Supermaven Chat can automatically upload compiler diagnostic messages (errors, warnings) alongside code context to provide error-aware suggestions and fixes. The mechanism is described as 'automatically uploading your code together with compiler diagnostic messages,' but specific language/compiler support and the upload trigger mechanism are undisclosed. This feature is Chat-only and not available in inline completion.
Unique: Automatic compiler diagnostic upload in Chat for error-aware suggestions, versus competitors (Copilot, Tabnine) that require manual error context or have limited diagnostic integration. Supermaven's approach reduces friction but with undisclosed language/compiler support.
vs alternatives: Automatic diagnostic upload reduces manual context-gathering compared to manual copy-paste; trade-off is undisclosed language support and unclear upload trigger mechanism.
Supermaven offers a 30-day free trial of the Pro tier ($10/month), providing full access to 1M token context window, largest model, style adaptation, and $5/month chat credits. No credit card is required to start the trial (implied), and trial conversion to paid is automatic after 30 days unless cancelled. Trial terms and auto-renewal policy are not explicitly detailed.
Unique: 30-day free trial of Pro tier with full feature access (1M context, largest model, chat credits), versus competitors (Copilot 2-month free trial, Tabnine free tier only) with different trial lengths and feature access. Supermaven's approach is generous but with undisclosed auto-renewal terms.
vs alternatives: Full Pro feature access during trial compared to limited free tier; trade-off is undisclosed auto-renewal policy and potential unexpected charges if not cancelled.
Supermaven requires internet connectivity and server-side inference; no offline mode or local inference capability is mentioned or available. All code completion requests are sent to Supermaven's backend servers for processing, and responses are returned over the network. This creates a hard dependency on network connectivity and Supermaven's service availability; if the service is down or network is unavailable, code completion is not available.
Unique: Supermaven has no offline mode or local inference capability; all processing is server-side. GitHub Copilot also requires server-side inference, but Tabnine offers local inference options for some use cases. Supermaven's lack of offline capability is a significant limitation for developers with connectivity constraints.
vs alternatives: Supermaven's server-side-only approach is comparable to GitHub Copilot; Tabnine offers local inference options, making Tabnine more suitable for offline work. Supermaven's lack of offline capability is a weakness vs. Tabnine.
Analyzes recent code edits and inferred coding patterns to adapt inline suggestions to match team conventions, naming patterns, and structural preferences. The mechanism is implicit (not explicit fine-tuning) and operates only on Pro/Team tiers, suggesting pattern learning from editor activity rather than explicit configuration. Free tier uses a single base model without personalization.
Unique: Implicit style adaptation via editor activity analysis without explicit configuration, versus competitors (Copilot, Tabnine) that require manual style guides or explicit fine-tuning. Supermaven's approach is transparent to the user but also non-configurable and undisclosed in mechanism.
vs alternatives: Requires no manual style configuration compared to tools requiring explicit style guides; trade-off is lack of transparency and inability to control or export learned styles.
Delivers code suggestions to the editor inline as the developer types, with a claimed baseline latency of 250ms from keystroke to suggestion display. The system uses a cloud inference backend and local editor plugin to minimize round-trip time. Latency claim is positioned against an unnamed competitor (783ms), but methodology is undisclosed and no independent verification is provided.
Unique: Claimed 250ms latency via optimized cloud inference pipeline and editor plugin architecture, versus competitors with higher latency (783ms unnamed baseline). Actual differentiation is undisclosed; mechanism may involve request batching, model quantization, or edge caching, but specifics are not public.
vs alternatives: Faster than unnamed competitor (250ms vs 783ms claimed); trade-off is cloud dependency and unverified latency claim with no SLA or performance guarantee.
Provides native editor extensions for VS Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), and Neovim, enabling inline suggestion rendering, hotkey-driven chat access, and compiler diagnostic integration directly within the editor. Each plugin variant is maintained separately and integrates with the editor's native autocomplete UI, keybinding system, and file context APIs.
Unique: Native plugins for three major editor ecosystems (VS Code, JetBrains, Neovim) with integrated chat and diff application, versus competitors (Copilot, Tabnine) that support broader editor ecosystems but with less deep integration in some cases. Supermaven's approach prioritizes depth over breadth.
vs alternatives: Deep integration with VS Code and JetBrains (native autocomplete UI, hotkey system) compared to web-based tools or lighter integrations; trade-off is limited editor support (no Sublime, Vim, Emacs) and undisclosed Neovim support details.
+5 more capabilities