| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 5 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Evaluates the correctness and quality of tool calls made by MCP servers by submitting them to an LLM for scoring against expected outcomes. Uses a prompt-based evaluation framework that sends tool call traces (input parameters, outputs, side effects) to Claude or other LLMs, which return structured scores (0-1 range) and reasoning. Integrates with GitHub Actions to run evaluations on every commit or pull request, storing results as workflow artifacts or check runs.
Unique: Specifically designed for MCP server validation using LLM-based scoring within GitHub Actions, providing automated quality gates for tool implementations without requiring manual test case writing. Uses MCP protocol semantics to extract and evaluate tool call traces directly from server responses.
vs alternatives: More specialized for MCP servers than generic LLM evaluation frameworks, and integrates natively with GitHub Actions workflows rather than requiring separate test infrastructure or external platforms.
Provides a reusable GitHub Action that can be invoked in CI/CD pipelines to run MCP tool evaluations on every push, pull request, or scheduled trigger. Handles workflow orchestration including: spinning up MCP server instances, executing test tool calls, collecting results, and reporting back to GitHub (check runs, status badges, PR comments). Manages authentication with LLM providers and stores evaluation results as workflow artifacts for historical tracking.
Unique: Native GitHub Actions integration that treats MCP server evaluation as a first-class CI/CD step, with built-in support for check runs, PR comments, and artifact storage rather than requiring custom glue code.
vs alternatives: Simpler to set up than building custom CI/CD logic or using generic test runners, because it understands MCP protocol semantics and GitHub Actions conventions natively.
Implements a scoring engine that sends tool call traces to an LLM with a structured evaluation rubric, receiving back numeric scores (0-1) and reasoning. The rubric defines evaluation criteria (correctness, completeness, error handling, performance) and the LLM applies these criteria to assess whether a tool call produced the expected outcome. Supports custom rubrics via prompt templates, allowing teams to define domain-specific evaluation criteria. Returns both individual tool call scores and aggregated metrics across test suites.
Unique: Uses LLM-based rubric evaluation specifically for MCP tool calls, allowing semantic assessment of tool correctness rather than relying on brittle regex or assertion-based testing. Supports custom rubrics to encode domain-specific evaluation logic.
vs alternatives: More flexible than assertion-based testing for complex tool outputs, and more interpretable than black-box ML-based evaluation because it provides LLM reasoning alongside scores.
Orchestrates the execution of test cases against an MCP server by: (1) starting the MCP server process, (2) invoking specified tool calls with test parameters, (3) capturing outputs and side effects, (4) collecting results into a structured format for evaluation. Handles MCP protocol communication (JSON-RPC over stdio or HTTP), manages server lifecycle (startup, shutdown, error handling), and normalizes tool call results into a consistent schema for downstream evaluation. Supports both local server instances and remote MCP servers.
Unique: Handles full MCP protocol lifecycle management (server startup, JSON-RPC communication, result collection) specifically for test execution, abstracting away MCP protocol details from evaluation logic.
vs alternatives: More complete than manual tool invocation because it manages server lifecycle and normalizes results, and more MCP-aware than generic test runners that don't understand MCP semantics.
Generates and publishes evaluation results back to GitHub using multiple reporting channels: check runs (pass/fail status on commits), PR comments (detailed evaluation summaries), workflow artifacts (raw evaluation logs), and status badges. Formats results for human readability (markdown tables, charts) and machine readability (JSON exports). Supports threshold-based pass/fail decisions to block PRs or trigger notifications. Integrates with GitHub's check runs API to provide inline feedback on specific commits.
Unique: Multi-channel reporting that leverages GitHub's native check runs and PR comment APIs to provide contextual feedback at the point of code review, rather than requiring developers to check a separate dashboard.
vs alternatives: More integrated into GitHub's native workflow than external dashboards or email reports, reducing friction for developers to see and act on evaluation results.
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-evals at 21/100. mcp-evals 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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