Supermaven ranks higher at 71/100 vs @tyk-technologies/api-to-mcp at 23/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | @tyk-technologies/api-to-mcp | Supermaven |
|---|---|---|
| Type | MCP Server | Extension |
| UnfragileRank | 23/100 | 71/100 |
| Adoption | 0 | 1 |
| Quality | 0 | 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 5 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Parses OpenAPI 3.0+ specifications and generates TypeScript/JavaScript MCP tool implementations that conform to the Model Context Protocol specification. The generator introspects OpenAPI operation definitions (paths, methods, parameters, request/response schemas) and emits executable MCP tool code with proper schema validation, error handling, and protocol compliance. Uses AST-based code generation to produce idiomatic, type-safe tool wrappers that can be immediately integrated into MCP servers.
Unique: Directly bridges OpenAPI specifications to MCP protocol by parsing operation metadata and generating protocol-compliant tool definitions with schema-aware parameter binding, eliminating manual tool definition boilerplate for REST API integration
vs alternatives: Faster than manual MCP tool coding for multi-endpoint APIs because it automates schema extraction and tool scaffolding from OpenAPI specs, whereas alternatives require hand-writing each tool definition
Transforms OpenAPI parameter definitions (path, query, header, body) into MCP tool input schemas with proper type inference, validation constraints, and required/optional field marking. Maps OpenAPI JSON Schema constraints (minLength, maxLength, pattern, enum, minimum, maximum) to MCP schema equivalents, ensuring generated tools enforce the same validation rules as the original API specification. Handles complex nested objects and array types through recursive schema traversal.
Unique: Performs bidirectional constraint analysis between OpenAPI JSON Schema and MCP input schemas, preserving validation semantics (min/max, patterns, enums) to ensure LLM-generated tool calls comply with API requirements without additional validation layers
vs alternatives: More constraint-preserving than generic schema converters because it specifically maps OpenAPI validation rules to MCP equivalents, preventing invalid API calls that would fail at runtime
Generates boilerplate MCP tool implementations that include HTTP client setup, request/response handling, and error transformation logic. The scaffolding creates tool functions that accept MCP input objects, construct HTTP requests using the OpenAPI operation definition, execute calls against a configurable API base URL, and transform HTTP responses back into MCP-compatible output. Includes error handling patterns for HTTP status codes, network failures, and response parsing errors with appropriate MCP error reporting.
Unique: Generates complete HTTP integration code including request construction, response parsing, and error transformation — not just tool signatures — allowing generated tools to execute immediately without additional client setup
vs alternatives: More complete than stub generators because it includes working HTTP client code, whereas alternatives require developers to manually implement request/response handling
Maps individual OpenAPI operations (GET /users/{id}, POST /users, etc.) to discrete MCP tool definitions with appropriate naming, descriptions, and input/output schemas. Extracts operation metadata (summary, description, tags, operationId) from OpenAPI and uses it to generate human-readable MCP tool names and descriptions. Creates separate tool definitions for each operation, allowing LLMs to discover and invoke specific API endpoints as independent tools rather than a monolithic API wrapper.
Unique: Creates one MCP tool per OpenAPI operation with metadata-driven naming and descriptions, enabling LLMs to discover and invoke specific endpoints as independent tools rather than treating the API as a single monolithic interface
vs alternatives: More granular than wrapper-based approaches because each operation becomes a discoverable tool, giving LLMs better visibility into available actions compared to single-tool wrappers
Generates TypeScript type definitions for all OpenAPI request and response schemas, enabling type-safe tool implementations and IDE autocomplete support. Converts OpenAPI JSON Schema definitions into TypeScript interfaces with proper typing for primitive types, objects, arrays, and union types. Includes support for schema references ($ref) and generates type files that can be imported alongside generated tool code for full type safety during development.
Unique: Generates complete TypeScript type definitions from OpenAPI schemas, enabling full type safety in generated tool code with IDE support, rather than generating untyped JavaScript that requires manual type annotations
vs alternatives: More developer-friendly than untyped code generation because it provides compile-time type checking and IDE autocomplete, reducing runtime errors compared to dynamically-typed alternatives
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 @tyk-technologies/api-to-mcp at 23/100. @tyk-technologies/api-to-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.
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