| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 8 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Exposes GraphQL schema as a named MCP resource (graphql-schema) that LLMs can access through the Model Context Protocol. The server implements schema discovery by either introspecting a live GraphQL endpoint using the GraphQL introspection query or reading a pre-cached local schema file, then serializes the complete type system (types, fields, arguments, directives) as a structured resource that LLM clients can reference in their context without re-fetching.
Unique: Implements schema exposure as a first-class MCP resource rather than a tool output, allowing LLM clients to reference the schema in their context window persistently and efficiently without repeated tool calls. Supports both live endpoint introspection and local schema file fallback for offline/cached scenarios.
vs alternatives: Unlike REST API documentation tools that require LLMs to parse markdown specs, mcp-graphql provides structured, queryable schema metadata that LLMs can reason about directly, and unlike generic GraphQL clients, it's optimized for LLM context management via MCP's resource protocol.
Implements a query-graphql tool that accepts a GraphQL query string and optional variables object, then executes the query against a configured GraphQL endpoint using HTTP POST with proper header injection and response parsing. The tool validates query syntax before execution, binds variables to the query using GraphQL's variable substitution mechanism, and returns the full response (data + errors) to the LLM, enabling dynamic query construction and parameterized operations.
Unique: Implements query execution as an MCP tool with built-in variable binding support, allowing LLMs to construct parameterized queries without string interpolation. Includes mutation-safety by default (disabled unless explicitly enabled) and passes through full GraphQL response semantics (data + errors) rather than flattening results.
vs alternatives: More secure than generic HTTP tools because it enforces GraphQL syntax and can disable mutations by default; more flexible than pre-built query libraries because it allows LLMs to construct arbitrary queries dynamically; cleaner than REST API wrappers because GraphQL's type system provides better context for LLM reasoning.
Implements a full Model Context Protocol server using the @modelcontextprotocol/sdk that manages the complete MCP lifecycle: server initialization with name/version metadata, resource and tool registration, stdio-based bidirectional communication with MCP clients, and graceful shutdown. The server uses Node.js stdio streams (stdin/stdout) as the transport layer, enabling seamless integration with MCP-compatible clients like Claude Desktop and Cline without requiring HTTP/WebSocket infrastructure.
Unique: Uses Node.js stdio streams as the MCP transport layer, eliminating the need for HTTP/WebSocket infrastructure and enabling direct process-based communication. Implements full MCP server semantics including resource listing, tool registration, and bidirectional message handling within a single TypeScript process.
vs alternatives: Simpler deployment than HTTP-based MCP servers because it requires no port binding or network configuration; more efficient than REST wrappers because it uses MCP's native protocol; better integrated with Claude Desktop than generic GraphQL clients because it follows MCP conventions.
Implements configuration management through environment variables (ENDPOINT, HEADERS, ALLOW_MUTATIONS, NAME, SCHEMA) that control server behavior at startup. The system supports a fallback mechanism where if a SCHEMA file path is provided, the server reads the local schema file instead of introspecting the live endpoint, enabling offline operation and schema caching. Headers are parsed from a JSON string in the HEADERS env var and injected into all GraphQL requests, supporting authentication tokens and custom headers without code changes.
Unique: Implements dual-mode schema loading: live introspection from endpoint OR local file fallback, allowing the same server binary to work offline or online. Uses JSON-parsed headers from env vars rather than individual header env vars, reducing configuration surface area.
vs alternatives: More flexible than hardcoded configuration because it supports multiple deployment scenarios (live endpoint, cached schema, different auth methods); cleaner than config files because it integrates with standard container/cloud environment variable patterns; safer than CLI args because secrets aren't exposed in process listings.
Implements a security control that blocks GraphQL mutation operations by default (ALLOW_MUTATIONS=false) and only allows them when explicitly enabled via environment variable. The server validates incoming GraphQL queries to detect mutation operations (queries containing 'mutation' keyword or mutation root types) and rejects them with an error message if mutations are disabled, preventing accidental or malicious data modification through LLM-generated queries.
Unique: Implements mutation blocking at the MCP server level rather than relying on endpoint-level permissions, providing a fail-safe control that works regardless of backend configuration. Uses explicit opt-in (ALLOW_MUTATIONS=true) rather than opt-out, defaulting to the safer posture.
vs alternatives: More reliable than relying on GraphQL endpoint permissions because it blocks mutations before they reach the backend; simpler than role-based access control because it's a binary on/off switch; better for LLM safety because it prevents the LLM from even attempting mutations unless explicitly enabled.
Implements a header injection mechanism that parses a JSON string from the HEADERS environment variable and injects those headers into every HTTP request sent to the GraphQL endpoint. This enables passing authentication tokens (Bearer tokens, API keys), custom headers (User-Agent, X-Custom-Header), and other request metadata without modifying the query execution logic. Headers are applied uniformly to all introspection and query execution requests.
Unique: Implements header injection via JSON-parsed environment variable rather than individual env vars per header, reducing configuration complexity. Headers are applied uniformly to all requests (introspection and queries) without requiring per-request customization.
vs alternatives: Cleaner than passing headers as CLI arguments because secrets aren't exposed in process listings; more flexible than hardcoded auth because it supports any header type; simpler than implementing OAuth/OIDC because it works with any authentication scheme that uses HTTP headers.
Implements response handling that returns the complete GraphQL response object (including both 'data' and 'errors' fields) to the LLM, preserving GraphQL's native error semantics. When a GraphQL query returns errors (validation errors, resolver errors, authentication failures), the server passes the full error objects back to the LLM rather than throwing exceptions or flattening the response, allowing the LLM to reason about partial failures and retry logic.
Unique: Preserves GraphQL's native response semantics by returning both data and errors fields, rather than converting errors to exceptions or flattening responses. Allows LLMs to reason about partial failures and error types without additional parsing.
vs alternatives: More informative than REST APIs that return HTTP status codes because GraphQL errors include structured error objects; more transparent than error-hiding wrappers because it exposes the full response; better for LLM reasoning because it preserves GraphQL's dual-field response model.
Implements a schema fallback mechanism that reads GraphQL schema from a local file (specified via SCHEMA env var) instead of introspecting a live endpoint. The server supports both GraphQL SDL (Schema Definition Language) and JSON introspection format, allowing pre-cached schemas to be used for offline operation or to avoid repeated introspection calls. This enables the same server binary to work with cached schemas in development or when the endpoint is temporarily unavailable.
Unique: Implements dual-mode schema loading (live introspection OR local file) with automatic fallback, allowing the same server binary to work in multiple deployment scenarios. Supports both SDL and JSON introspection formats without requiring explicit format specification.
vs alternatives: More flexible than endpoint-only introspection because it supports offline operation; simpler than schema registry solutions because it uses local files; better for version control than dynamic introspection because schemas can be committed to git.
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-graphql at 28/100. mcp-graphql 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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