| 0 |
| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Implements Microsoft Authentication Library (MSAL) device code flow to authenticate users without requiring interactive browser login, storing tokens securely in the OS credential store via Keytar for persistence across sessions. The flow generates a device code that users enter on a browser, while the server polls Microsoft's token endpoint until authentication completes, then caches the refresh token locally for subsequent API calls without re-authentication.
Unique: Uses MSAL device code flow with OS-level credential storage (Keytar) instead of file-based token persistence, eliminating plaintext token files and leveraging platform-native security (Windows Credential Manager, macOS Keychain, Linux Secret Service)
vs alternatives: More secure than custom OAuth implementations because it delegates token management to MSAL and OS credential stores, and more practical than service principal auth for user-delegated scenarios where interactive setup is acceptable
Implements the Model Context Protocol (MCP) server specification to expose Microsoft 365 capabilities as callable tools through stdin/stdout communication. The server registers a tool registry containing Graph API wrappers, handles tool invocation requests from MCP clients (like Claude), marshals parameters, executes Graph API calls, and returns formatted responses back through the MCP protocol, enabling any MCP-compatible client to access Microsoft 365 services.
Unique: Implements full MCP server specification with tool registry pattern, allowing dynamic tool registration and parameter validation at the protocol level, rather than ad-hoc function calling. Uses Commander.js for CLI argument parsing and MicrosoftGraphServer as the orchestration layer that bridges MCP protocol and Graph API.
vs alternatives: More standardized than custom REST APIs because it follows the MCP specification, enabling compatibility with multiple AI clients without custom integration code per client. More flexible than direct Graph API exposure because it abstracts authentication, error handling, and response formatting.
Implements a Graph API HTTP client that handles authentication header injection, request formatting, response parsing, and error handling. Includes retry logic for transient failures (429 rate limits, 5xx errors) with exponential backoff, and structured error responses that map Graph API errors to user-friendly messages. Manages token refresh automatically when access tokens expire.
Unique: Implements automatic token refresh by detecting 401 responses and requesting new tokens from the authentication manager, eliminating the need for manual token management in tools. Uses exponential backoff for retry logic with configurable max retries.
vs alternatives: More reliable than raw fetch calls because it includes automatic retry and token refresh logic. More maintainable than custom HTTP wrappers because it centralizes error handling and authentication.
Serves as the main orchestration component that initializes the MCP server, sets up authentication, registers all Graph API tools, and manages the server lifecycle. Coordinates between the CLI parser, authentication manager, Graph client, and MCP protocol handler. Implements tool registration by wrapping Graph API operations with parameter validation and response formatting.
Unique: Implements centralized tool registration through a single orchestration layer that wraps Graph API operations with consistent parameter validation and error handling, rather than scattered tool definitions. Uses dependency injection pattern to pass authentication manager and Graph client to tools.
vs alternatives: More maintainable than distributed tool registration because all tools are registered in one place. More testable than monolithic server code because orchestration logic is separated from protocol handling.
Wraps Microsoft Graph API email endpoints to enable reading message lists with filtering/pagination, retrieving full message bodies with attachments, sending emails with recipients and attachments, and managing folder operations (move, delete, archive). Implements Graph API query syntax for filtering by sender, subject, date ranges, and read status, with support for attachment streaming and MIME message composition.
Unique: Leverages Graph API's OData query syntax for server-side filtering and pagination, reducing payload size compared to client-side filtering. Implements attachment handling through Graph API's /attachments endpoint with streaming support for large files.
vs alternatives: More reliable than IMAP/SMTP because it uses Microsoft's official Graph API with built-in retry logic and modern authentication. More feature-complete than basic SMTP because it supports folder operations, read status, and attachment metadata without custom parsing.
Exposes Microsoft Graph Calendar API to create, read, update, and delete calendar events with support for attendees, meeting times, reminders, and recurrence patterns. Implements event creation with automatic meeting invitation sending, attendee response tracking, and conflict detection through Graph API's calendar view queries. Supports recurring event patterns (daily, weekly, monthly) and timezone-aware scheduling.
Unique: Uses Graph API's calendar view queries with time range filtering to detect conflicts and availability, rather than fetching all events. Implements attendee response tracking through Graph API's attendeeAvailability property.
vs alternatives: More integrated than CalDAV because it handles meeting invitations and attendee responses natively through Graph API. More reliable than custom calendar parsing because it uses Microsoft's official API with built-in conflict detection.
Wraps Microsoft Graph DriveItem API to list files and folders, upload/download files, create folders, and manage file metadata. Implements path-based file access (e.g., '/Documents/Report.xlsx') that translates to Graph API's drive item hierarchy navigation, supporting file streaming for large uploads/downloads and metadata queries for file properties (size, modified date, sharing status).
Unique: Implements path-based file access abstraction that translates human-readable paths to Graph API's drive item IDs, hiding the complexity of hierarchical navigation. Uses Graph API's /content endpoint for streaming file uploads/downloads.
vs alternatives: More user-friendly than raw Graph API because it supports path-based access instead of requiring drive item IDs. More reliable than WebDAV because it uses Microsoft's official API with built-in authentication and error handling.
Exposes Microsoft Graph Excel API to read and write cell values, create worksheets, and execute formulas within Excel files stored in OneDrive. Implements OneNote API access to read notebook structure, create pages, and append content. Both services use Graph API's workbook sessions for transactional consistency and support batch operations for multiple cell updates.
Unique: Uses Graph API's workbook session management for transactional consistency across multiple cell updates, preventing race conditions in concurrent scenarios. Implements OneNote page append operations through Graph API's /content endpoint with HTML content support.
vs alternatives: More reliable than OpenPyXL or similar libraries because it works with cloud-stored files without local download/upload cycles. More integrated than REST-based Excel APIs because it leverages Microsoft's official Graph API with built-in session management.
+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 ms-365-mcp-server at 32/100. ms-365-mcp-server leads on ecosystem, while Supermaven is stronger on adoption and quality.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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