| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Exposes Figma's REST API document hierarchy through MCP tools, enabling programmatic access to file structure, layers, components, and design tokens. Works by wrapping Figma's GET /v1/files/{file_id} endpoint and parsing the hierarchical JSON response into queryable node structures with metadata about frame bounds, fill colors, typography, and component references.
Unique: Bridges Figma's REST API into MCP's standardized tool interface, allowing LLM agents to query design files without custom API client code. Uses MCP's resource-based architecture to expose Figma documents as queryable resources rather than one-off API calls.
vs alternatives: Simpler than building custom Figma API integrations because MCP handles authentication, request formatting, and response parsing; more accessible to non-frontend developers than direct REST API calls.
Resolves component instances to their main component definitions and tracks applied overrides (property changes, nested swaps). Implemented by following Figma's componentId references through the document tree and comparing instance properties against the main component's defaults to identify which properties have been overridden.
Unique: Automatically maps component instances to their main definitions and extracts override deltas by comparing instance properties against component defaults — a pattern not exposed directly in Figma's UI, requiring API-level traversal.
vs alternatives: More precise than manual component audits because it programmatically identifies all overrides; more efficient than Figma's built-in component search because it can filter by override patterns, not just component name.
Extracts constraint rules (fixed/flexible width/height, left/right/center alignment) and responsive behavior metadata from Figma elements. Parses constraint properties to understand how elements resize relative to their parent, enabling responsive layout code generation.
Unique: Extracts Figma's constraint system (which defines how elements resize relative to parents) into structured format, enabling tools to generate responsive CSS that preserves design intent without manual constraint transcription.
vs alternatives: More precise than manual constraint documentation because it extracts constraints programmatically; more useful than visual inspection because it captures all constraint rules in machine-readable format.
Extracts shadow, blur, and other visual effects from Figma elements, normalizing them to CSS or design token format. Works by parsing Figma's effects array (shadows, blurs, background blurs) and converting to standard CSS syntax or design token representations.
Unique: Normalizes Figma's effects system (shadows, blurs, background blurs) into CSS and design token formats, enabling tools to generate visual effects without manual conversion or approximation.
vs alternatives: More accurate than manual effect transcription because it uses Figma's authoritative effect data; more flexible than static effect exports because it supports multiple output formats.
Extracts design tokens (colors, typography, spacing, shadows) from Figma styles and component properties, normalizing them into structured JSON or CSS variable format. Works by parsing Figma's style definitions (fill colors, text styles, effects) and mapping them to token categories, then generating standardized output formats compatible with design token standards (Design Tokens Community Group format).
Unique: Normalizes Figma's style system (which uses hierarchical naming and mixed property types) into standardized token formats by parsing style metadata and applying configurable naming conventions and grouping rules.
vs alternatives: More flexible than Figma's native export because it supports multiple output formats and can apply custom naming transformations; more reliable than manual token transcription because it's automated and version-controlled.
Registers Figma API operations as MCP tools with auto-generated JSON schemas, enabling LLM agents to discover and call Figma capabilities through a standardized interface. Implemented by wrapping Figma REST endpoints with MCP's tool schema format, generating input/output schemas from Figma API specifications, and handling authentication transparently through MCP's credential management.
Unique: Implements MCP's tool registration pattern for Figma, automatically generating JSON schemas from Figma API specs and handling credential injection through MCP's standardized authentication flow — eliminating the need for agents to manage API keys or format requests manually.
vs alternatives: More standardized than custom Figma API wrappers because it uses MCP's protocol, enabling compatibility with any MCP-aware agent; more discoverable than direct API calls because agents can introspect available tools and their schemas.
Lists accessible Figma files and pages with metadata (name, last modified, owner, thumbnail URL) by calling Figma's REST endpoints for team/project resources. Returns structured data about available design files, enabling agents or applications to discover and select files without hardcoding file IDs.
Unique: Exposes Figma's team/project resource hierarchy through MCP, allowing agents to dynamically discover files rather than requiring hardcoded file IDs — a pattern that enables flexible, multi-file workflows.
vs alternatives: More flexible than hardcoded file IDs because it discovers files dynamically; more efficient than manual file selection because it can filter and sort by metadata programmatically.
Extracts bounding box coordinates, dimensions, and layout properties (auto-layout, constraints) for frames and artboards in a Figma file. Implemented by parsing the node tree and extracting x, y, width, height properties along with layout metadata, enabling spatial analysis and layout-aware code generation.
Unique: Extracts layout geometry and auto-layout rules from Figma's node properties, enabling downstream tools to understand spatial relationships without visual rendering — a pattern useful for layout-aware code generation.
vs alternatives: More precise than visual analysis because it uses Figma's authoritative layout data; more efficient than screenshot-based layout detection because it works with structured data.
+4 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 figma-mcp at 27/100. figma-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