Supermaven ranks higher at 71/100 vs @moralisweb3/api-mcp-server at 21/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | @moralisweb3/api-mcp-server | Supermaven |
|---|---|---|
| Type | MCP Server | Extension |
| UnfragileRank | 21/100 | 71/100 |
| Adoption | 0 | 1 |
| 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 Moralis Web3 API endpoints as Model Context Protocol (MCP) tools, allowing LLM agents and Claude instances to query blockchain data (transactions, balances, NFTs, token transfers) through standardized MCP tool schemas. Implements MCP server specification with JSON-RPC 2.0 transport, converting REST API calls into MCP tool definitions that Claude and other MCP-compatible clients can discover and invoke with type-safe arguments.
Unique: Bridges Moralis Web3 REST API into MCP protocol specification, enabling Claude and other LLM agents to natively discover and call blockchain queries as first-class tools without custom adapter code. Uses MCP's standardized tool schema definition to expose Moralis endpoints with type validation.
vs alternatives: Provides native MCP integration for Moralis data vs building custom tool wrappers or using generic HTTP-calling tools, reducing integration boilerplate and enabling better LLM reasoning over blockchain queries.
Automatically generates MCP tool schemas from Moralis API specifications, mapping REST endpoint parameters, response structures, and authentication requirements into MCP-compliant tool definitions. Implements schema generation logic that converts OpenAPI/REST metadata into JSON Schema format compatible with MCP tool discovery, enabling clients to understand argument types, required fields, and return value structures without manual schema authoring.
Unique: Implements automated schema generation pipeline that converts Moralis API specifications into MCP-compliant tool definitions with full type information, eliminating manual schema authoring for each blockchain query endpoint.
vs alternatives: Reduces integration effort vs manually writing MCP tool schemas for each API endpoint, and provides better type safety than generic HTTP-calling tools that lack structured argument validation.
Routes blockchain queries to appropriate Moralis API endpoints based on chain identifier (chainId, chain name, or network slug), abstracting chain-specific endpoint differences. Implements routing logic that maps user-provided chain references to Moralis-supported networks (Ethereum, Polygon, BSC, Solana, Arbitrum, Optimism, etc.) and forwards requests to correct API endpoints with chain-specific authentication and rate-limit handling.
Unique: Implements chain routing layer that abstracts Moralis' multi-chain API surface, allowing agents to query different blockchains with identical tool calls and chain parameter, reducing agent logic complexity for cross-chain operations.
vs alternatives: Simpler than building custom chain-detection and routing logic in agent code, and more maintainable than hardcoding chain-specific endpoints throughout agent workflows.
Implements MCP server lifecycle management (initialization, tool registration, request handling, shutdown) using stdio-based JSON-RPC 2.0 transport for communication with MCP clients. Manages server startup, tool discovery handshake, request/response serialization, and graceful shutdown, enabling Claude Desktop and other MCP clients to spawn and communicate with the server process via standard input/output streams.
Unique: Provides complete MCP server lifecycle implementation with stdio JSON-RPC 2.0 transport, handling all protocol-level concerns (message serialization, request routing, error handling) so developers only need to define blockchain query tools.
vs alternatives: Eliminates need to implement MCP protocol details manually; developers can focus on API integration rather than transport and serialization logic.
Manages Moralis API key authentication, injecting credentials into outbound API requests and handling authentication errors. Implements credential loading from environment variables or configuration, request header injection with API key, and error handling for invalid or rate-limited credentials. Abstracts authentication complexity from tool implementations so blockchain queries automatically include proper credentials.
Unique: Centralizes Moralis API key management at the MCP server level, injecting credentials into all outbound requests and handling authentication errors transparently, preventing credential leakage to client code.
vs alternatives: More secure than passing API keys through tool arguments or storing them in agent prompts; provides centralized credential management vs distributed key handling across multiple tools.
Implements error handling for blockchain queries, catching Moralis API errors (invalid address, unsupported chain, rate limits, network errors) and returning structured error responses to MCP clients. Maps HTTP error codes and API-specific error messages into MCP error format, providing actionable error context (e.g., 'invalid Ethereum address format', 'chain not supported by Moralis') so agents can handle failures gracefully.
Unique: Translates Moralis API errors into structured MCP error responses with actionable context, enabling agents to distinguish between input validation failures, unsupported operations, and transient API issues.
vs alternatives: Better error context than generic HTTP error codes; agents can implement intelligent retry and fallback logic based on specific error types rather than generic failure handling.
Exposes available blockchain query tools through MCP tool discovery mechanism, providing clients with tool names, descriptions, argument schemas, and usage documentation. Implements MCP's tools/list and tools/describe endpoints, allowing Claude and other clients to enumerate available blockchain queries and understand their parameters without reading external documentation.
Unique: Implements MCP tool discovery protocol, making blockchain queries self-documenting and discoverable by clients without external documentation or manual tool registration.
vs alternatives: Better discoverability than tools requiring manual registration or external documentation; clients can introspect available queries at runtime and understand parameters without consulting separate docs.
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 @moralisweb3/api-mcp-server at 21/100. @moralisweb3/api-mcp-server 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.
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