| Quality |
| 0 |
| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol standard to register SEO tools as discoverable resources that AI agents can invoke. Uses a modular architecture where BaseModule abstract class provides a common interface for tool registration, and McpServer centralizes tool discovery and client connection handling. Each tool is registered with structured metadata (name, description, input schema) that MCP clients can query to understand available capabilities without hardcoding tool knowledge.
Unique: Uses MCP protocol standard rather than custom REST/gRPC wrappers, enabling seamless integration with Claude and other MCP-aware AI agents without custom client libraries. Implements hierarchical tool organization through BaseModule inheritance pattern, allowing selective module enable/disable through configuration.
vs alternatives: Provides standardized tool discovery and invocation compared to point-to-point API integrations, reducing client-side complexity and enabling multi-agent orchestration without tool-specific adapters.
Provides access to real-time search engine results from Google, Bing, and Yahoo through the SERP module, which translates MCP tool calls into DataForSEO SERP API requests. The SerpModule extends BaseModule and registers individual tools for different search queries and parameters. Handles authentication via DataForSEOClient, processes API responses, and returns structured SERP data including rankings, snippets, and metadata in a consistent JSON format.
Unique: Abstracts DataForSEO's SERP API complexity through MCP tool interface, enabling AI agents to query multi-engine search results with unified parameter schema. Implements response normalization across Google/Bing/Yahoo result formats into consistent JSON structure.
vs alternatives: Provides real-time multi-engine SERP data through standardized MCP interface compared to building custom SERP API clients, with built-in response normalization and agent-friendly parameter validation.
Implements tools that analyze market-level SEO trends by querying DataForSEO Labs data for emerging keywords, trending topics, and market shifts. Tools accept market/industry parameters and return trend analysis including rising keywords, declining topics, seasonal patterns, and market opportunity assessment. Implements time-series analysis on historical keyword data to identify patterns and forecast trends.
Unique: Performs time-series analysis on DataForSEO Labs historical keyword data to identify trends and forecast future demand. Implements market-level aggregation across multiple keywords to surface macro trends.
vs alternatives: Provides market-level trend analysis and forecasting through MCP tools compared to manual trend research, with built-in time-series analysis and seasonal pattern detection.
Provides BaseTool abstract class and module extension patterns that enable developers to add new tools for DataForSEO APIs not yet implemented in the server. Developers extend BaseTool, implement execute method with API call logic, and register the tool with a module. The framework handles MCP protocol integration, parameter validation, and response formatting automatically. Includes development guide and examples for adding new tools and modules.
Unique: Provides inheritance-based tool framework (BaseTool abstract class) enabling developers to extend server with new tools by implementing execute method. Handles MCP protocol integration automatically, reducing boilerplate.
vs alternatives: Enables custom tool development through abstract base class pattern compared to monolithic server, reducing code duplication and allowing incremental feature addition without modifying core server code.
Exposes DataForSEO's Keywords Data API through the KeywordsDataModule, enabling AI agents to retrieve keyword research metrics including search volume, CPC, competition level, and trend data. The module registers tools that translate keyword queries into DataForSEO API calls, aggregate metrics across data sources, and return structured keyword intelligence. Handles parameter validation for keyword lists, geographic targeting, and language selection before forwarding to the DataForSEO backend.
Unique: Aggregates keyword metrics from DataForSEO's proprietary database through MCP interface, normalizing multi-source data (Google Trends, Ads data, organic search signals) into unified keyword intelligence schema. Implements batch processing with automatic chunking for large keyword lists.
vs alternatives: Provides comprehensive keyword metrics (search volume + CPC + competition + trends) through single MCP tool compared to querying multiple SEO tools separately, with built-in batch processing and geographic market comparison.
Implements the OnPage module to provide website crawling and on-page SEO performance analysis through DataForSEO's OnPage API. Tools in this module accept target URLs and return structured crawl data including page metadata, technical SEO issues, content analysis, and performance metrics. The module handles crawl job submission, polling for completion, and result aggregation into a unified response format that AI agents can interpret for SEO recommendations.
Unique: Abstracts DataForSEO's asynchronous crawl job model through synchronous MCP tool interface with built-in polling and result aggregation. Normalizes crawl data across different site architectures (single-page, multi-domain, subdomain structures) into consistent schema.
vs alternatives: Provides comprehensive on-page analysis (technical SEO + content metrics + issue detection) through single MCP tool compared to manual crawling or multiple point tools, with automatic job polling and result aggregation.
Exposes DataForSEO Labs API through the DataForSEOLabsModule, providing access to proprietary SEO databases including historical SERP data, keyword difficulty scores, backlink metrics, and domain authority estimates. Tools in this module query DataForSEO's aggregated SEO intelligence database rather than real-time crawls, enabling historical analysis and trend identification. Implements caching strategies for frequently-accessed metrics to reduce API calls.
Unique: Provides access to DataForSEO's proprietary SEO intelligence database (not available through public APIs) through MCP interface, including historical SERP snapshots, algorithmic difficulty scores, and trend analysis. Implements optional response caching for expensive queries.
vs alternatives: Offers historical SEO data and proprietary metrics (keyword difficulty, opportunity scores) through standardized MCP interface compared to building custom DataForSEO Labs integrations, with built-in caching for frequently-accessed metrics.
Implements a modular architecture where functionality is organized into independent modules (SERP, KeywordsData, OnPage, DataForSEOLabs) that extend BaseModule abstract class. Each module registers its own set of tools and can be selectively enabled/disabled through configuration without modifying code. The McpServer loads enabled modules at startup and registers their tools, allowing operators to control which DataForSEO APIs are exposed to clients based on subscription tier or security policy.
Unique: Uses inheritance-based module system (BaseModule abstract class) rather than plugin architecture, enabling compile-time type safety while maintaining runtime module selection. Configuration-driven module loading allows operators to control API exposure without code changes.
vs alternatives: Provides selective API access control through modular architecture compared to monolithic API wrappers, enabling tiered feature access and easier maintenance as new DataForSEO APIs are added.
+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 mcp-server-typescript at 30/100. mcp-server-typescript 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