| Quality | 0 | 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 7 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Exposes AMap's geocoding API through the Model Context Protocol, allowing LLM agents to convert addresses to coordinates and coordinates to addresses. Implements MCP tool schema binding that wraps AMap REST API endpoints, handling request serialization, response parsing, and error translation into structured tool results that Claude and other MCP-compatible clients can consume.
Unique: Native MCP server implementation that directly exposes AMap geocoding as callable tools within LLM agents, rather than requiring custom API wrapper code — agents can invoke geocoding as a first-class operation without intermediate HTTP handling
vs alternatives: Tighter integration with Claude and MCP-compatible models compared to using raw AMap REST API, eliminating boilerplate tool definition and response parsing code
Wraps AMap's routing API (driving, walking, transit) through MCP tool schema, enabling LLM agents to request turn-by-turn directions, distance/duration estimates, and route alternatives. Translates AMap's polyline-encoded route geometry and step-by-step instructions into structured tool outputs that agents can reason about and present to users.
Unique: Exposes multi-modal routing (driving, walking, transit) as discrete MCP tools with unified response schema, allowing agents to reason about transport mode tradeoffs without custom parsing logic
vs alternatives: Simpler integration than building custom routing tool wrappers; agents can directly invoke routing without managing API response heterogeneity across transport modes
Implements MCP tool bindings for AMap's place search API, enabling agents to discover points of interest (restaurants, hotels, gas stations, etc.) by keyword, category, or proximity. Handles spatial queries (nearby search, polygon/circle search) and returns ranked results with ratings, contact info, and business hours, allowing agents to help users find relevant locations.
Unique: Exposes AMap's multi-criteria POI search (keyword, category, proximity, polygon) as a single unified MCP tool with flexible filtering, avoiding the need for agents to manage multiple API endpoints
vs alternatives: More comprehensive than simple keyword search; agents can combine spatial, categorical, and textual criteria in a single tool invocation without custom query composition
Wraps AMap's static map API through MCP, allowing agents to generate map images with customizable markers, polylines, and polygons for visualization in chat interfaces. Constructs image URLs with encoded geometry, styling parameters, and zoom levels, returning image URLs that clients can render or embed in responses.
Unique: Generates map images as MCP tool outputs, allowing agents to include geographic visualizations directly in responses without requiring separate image generation infrastructure
vs alternatives: Simpler than embedding interactive maps; agents can generate and present map images in a single tool call without client-side map library dependencies
Exposes AMap's distance matrix API through MCP, enabling agents to calculate distances and travel times between multiple origin-destination pairs in a single request. Supports driving, walking, and transit modes, returning a matrix of distances/durations that agents can use for optimization, comparison, or decision-making.
Unique: Batch distance calculation as a single MCP tool, allowing agents to reason about multi-point routing without issuing multiple individual route requests
vs alternatives: More efficient than sequential point-to-point routing calls; agents can analyze all pairwise distances in one operation, enabling optimization logic
Implements MCP tool binding for AMap's IP location API, enabling agents to determine geographic location from IP addresses. Returns city-level or more granular location data, allowing agents to infer user location context or validate geographic constraints without explicit user input.
Unique: Provides implicit location context to agents via IP lookup, enabling location-aware behavior without explicit user input or permission flows
vs alternatives: Simpler than requiring explicit location permission; agents can infer approximate location context automatically, though with accuracy tradeoffs
Implements the Model Context Protocol (MCP) server framework, handling bidirectional JSON-RPC communication with MCP clients (Claude, custom hosts), tool schema definition and validation, and request/response marshaling. Manages the lifecycle of tool invocations, error handling, and result serialization according to MCP specification.
Unique: Implements MCP server as the primary integration point, making AMap services first-class tools in MCP-compatible environments rather than requiring custom API wrapper code
vs alternatives: Standardized MCP protocol enables seamless integration with Claude and other MCP clients without custom tool definition or schema management
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 @amap/amap-maps-mcp-server at 21/100. @amap/amap-maps-mcp-server leads on ecosystem, while Supermaven is stronger on adoption and quality.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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