| 0 |
| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 11 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Exposes Chrome DevTools Protocol (CDP) as MCP resources and tools, allowing LLM agents to interact with browser automation and inspection through a standardized message-passing interface. Implements bidirectional communication between MCP clients and CDP endpoints, translating MCP tool calls into CDP commands and streaming CDP events back as resource updates.
Unique: Directly maps MCP tool schema to Chrome DevTools Protocol methods, eliminating the need for intermediate abstraction layers like Puppeteer; enables LLM agents to access low-level browser inspection and control primitives (DOM queries, network interception, JavaScript evaluation) without wrapper libraries
vs alternatives: More direct and lower-latency than Puppeteer/Playwright MCP wrappers because it translates MCP calls directly to CDP without additional process overhead or abstraction layers
Manages browser page lifecycle (navigation, reload, back/forward) and maintains context about the current page state (URL, title, DOM structure). Implements CDP Page domain methods wrapped as MCP tools, allowing agents to navigate to URLs, wait for page load events, and retrieve structured snapshots of page content for decision-making.
Unique: Exposes CDP Page domain as MCP tools with built-in wait-for-load semantics, allowing agents to express navigation intent declaratively ('navigate to URL and wait for load') rather than managing event listeners and timeouts manually
vs alternatives: Simpler than Playwright's page object model for MCP because it maps directly to CDP primitives without introducing additional state management or retry logic
Exposes current page state (DOM, metadata, network activity, console logs) as MCP resources that agents can subscribe to and monitor in real-time. Implements resource URIs for different page aspects (e.g., 'browser://page/dom', 'browser://page/console'), with automatic updates as page state changes, enabling agents to maintain contextual awareness without polling.
Unique: Implements MCP resource protocol for page state, allowing agents to subscribe to real-time updates rather than polling or managing CDP event listeners manually, providing a declarative interface to browser state
vs alternatives: More efficient than polling-based state checks because it streams updates as they occur, reducing latency and network overhead for long-running automation workflows
Provides MCP tools for querying the DOM using CSS selectors or XPath, retrieving element properties (text content, attributes, computed styles, bounding box), and inspecting element hierarchy. Implements CDP DOM domain methods with selector-based lookup, enabling agents to locate and analyze page elements without JavaScript execution.
Unique: Wraps CDP DOM.querySelector and DOM.getAttributes as MCP tools with structured output, allowing agents to query and inspect elements without writing JavaScript or managing CDP node IDs directly
vs alternatives: More efficient than Puppeteer's page.evaluate() for simple DOM queries because it uses CDP's native DOM domain instead of spinning up a JavaScript context
Simulates user interactions (click, type, scroll, hover, key press) by translating MCP tool calls into CDP Input domain commands. Implements element targeting via CSS selector or coordinates, with automatic scroll-into-view and focus management, enabling agents to interact with page elements without JavaScript injection.
Unique: Combines CDP Input domain (for low-level event injection) with element targeting via selectors, providing agents with high-level interaction primitives (click element by selector) without requiring coordinate calculation or JavaScript event handling
vs alternatives: More reliable than JavaScript-based click simulation because it uses CDP's native input injection, which properly triggers browser event handlers and respects z-index/visibility rules
Executes arbitrary JavaScript in the page context via CDP Runtime domain, allowing agents to evaluate expressions, call page functions, and access JavaScript objects. Implements serialization of return values to JSON, with support for primitive types, objects, and arrays, enabling agents to extract computed data or trigger page-specific logic.
Unique: Exposes CDP Runtime.evaluate as an MCP tool with automatic JSON serialization, allowing agents to execute arbitrary JavaScript without managing CDP protocol details or handling serialization errors manually
vs alternatives: More flexible than DOM-only queries for complex data extraction because it can access JavaScript state and call page functions, but requires careful error handling for non-serializable return values
Monitors network requests and responses via CDP Network domain, providing agents with visibility into HTTP traffic, response bodies, and request headers. Implements request/response logging with optional filtering by URL pattern or resource type, enabling agents to verify API calls, extract data from network responses, or detect failed requests.
Unique: Exposes CDP Network domain as MCP tools with structured request/response logging, allowing agents to monitor and analyze network traffic without writing custom CDP event listeners or managing request buffering
vs alternatives: More comprehensive than Puppeteer's request interception because it captures full response bodies and provides detailed timing metrics, but requires explicit enablement to avoid memory overhead
Captures console output (log, warn, error, info) and JavaScript errors via CDP Runtime domain, streaming them as MCP resources or tool responses. Implements log level filtering and error stack trace capture, enabling agents to monitor page health and detect runtime errors during automation.
Unique: Streams console and error events from CDP Runtime domain as MCP resources, allowing agents to monitor page health in real-time without polling or manual log extraction
vs alternatives: More immediate than checking page state after interactions because it captures errors as they occur, enabling agents to detect and respond to failures during automation
+3 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 browser-devtools-mcp at 27/100. browser-devtools-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