Supermaven ranks higher at 71/100 vs @softeria/ms-365-mcp-server at 26/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | @softeria/ms-365-mcp-server | Supermaven |
|---|---|---|
| Type | MCP Server | Extension |
| UnfragileRank | 26/100 | 71/100 |
| Adoption | 0 | 1 |
| Quality | 0 | 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 7 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Exposes Microsoft 365 resources (users, groups, mail, calendar, files, teams) through the Model Context Protocol by implementing MCP server endpoints that translate client requests into authenticated Graph API calls. Uses OAuth 2.0 client credentials or delegated auth flows to obtain tokens, then marshals HTTP requests to Microsoft's REST endpoints and transforms JSON responses back through MCP's tool/resource interface.
Unique: Implements MCP server pattern specifically for Graph API, translating MCP's tool/resource abstraction directly to Graph's OData query model and batch operations, enabling LLMs to compose complex M365 workflows without direct API knowledge
vs alternatives: Provides native MCP integration for M365 (vs. generic REST API wrappers), allowing Claude and other MCP clients to access Office 365 with standardized tool calling rather than custom API client libraries
Implements MCP tools to list, read, and compose emails by wrapping Graph API's /me/messages, /me/mailFolders, and /me/sendMail endpoints. Supports filtering by folder, sender, date range, and subject via OData query parameters; retrieves message bodies in both plain text and HTML; handles attachments as metadata references. Composition creates draft messages with recipients, subject, body, and attachment references before sending.
Unique: Exposes Exchange Online mailbox operations through MCP's tool interface with OData filtering support, allowing LLMs to compose natural-language email queries (e.g., 'unread emails from my manager this week') that map to efficient Graph API filters
vs alternatives: Simpler than building custom IMAP/SMTP clients; leverages Graph API's native filtering and pagination, avoiding the complexity of MIME parsing and IMAP protocol state management
Provides MCP tools to list, read, and create calendar events by calling Graph API's /me/calendarview, /me/events, and /me/events POST endpoints. Supports time-range queries via startDateTime/endDateTime parameters, retrieves event details (title, time, attendees, location, body), and creates new events with attendee lists and reminders. Handles recurring events and timezone conversions via IANA timezone identifiers.
Unique: Wraps Graph API's calendarview endpoint with timezone-aware time range queries, enabling LLMs to ask natural questions about availability ('am I free next Tuesday 2-3pm?') that translate to efficient Graph API calls with proper timezone handling
vs alternatives: More flexible than CalDAV clients for querying; Graph API's calendarview endpoint is optimized for range queries and attendee expansion, avoiding the need to parse iCalendar objects or manage subscription state
Implements MCP tools to list, read metadata, and retrieve file contents from OneDrive and SharePoint via Graph API's /me/drive, /drives/{driveId}/items, and /sites/{siteId}/drive endpoints. Supports hierarchical path navigation, file search by name/type, and retrieval of file metadata (size, modified date, sharing status). File contents are returned as base64-encoded data or text previews depending on file type.
Unique: Provides path-based file navigation through Graph API's item hierarchy, allowing LLMs to traverse OneDrive/SharePoint like a filesystem while leveraging Graph's native metadata and sharing APIs
vs alternatives: Simpler than WebDAV or direct SharePoint REST calls; Graph API abstracts away site/drive ID complexity and provides unified interface for both OneDrive and SharePoint
Exposes MCP tools to query Azure AD users and groups via Graph API's /users, /groups, and /me/memberOf endpoints. Supports filtering by display name, email, job title, department, and group membership; retrieves user profiles (name, email, phone, office location, manager) and group details (members, owners, description). Implements pagination for large result sets using Graph API's skipToken mechanism.
Unique: Provides OData-filtered directory queries through MCP, enabling LLMs to compose natural-language people searches ('find all engineers in the sales department') that map to efficient Graph API filter expressions
vs alternatives: More efficient than LDAP queries for cloud-native Azure AD; Graph API's OData filtering is optimized for directory searches and includes modern attributes (manager, department) without LDAP schema complexity
Implements MCP tools to list Teams, channels, and messages via Graph API's /teams, /teams/{teamId}/channels, and /teams/{teamId}/channels/{channelId}/messages endpoints. Supports message filtering by date range and sender, retrieves message threads with replies, and accesses channel metadata (description, topic, members). Messages include sender info, timestamps, and reply counts for conversation context.
Unique: Exposes Teams channel messages through MCP with conversation threading support, allowing LLMs to retrieve message context and replies without manually navigating Teams UI or managing conversation state
vs alternatives: Simpler than Teams SDK for message retrieval; Graph API abstracts away Teams client complexity and provides unified REST interface for both channel and chat access
Implements the MCP server protocol by registering tools and resources with the MCP client, handling request/response serialization, and managing server initialization. Uses Node.js event emitters to handle incoming tool calls, validates request parameters against defined schemas, and returns structured responses. Manages OAuth token lifecycle (refresh, expiry handling) and connection state between MCP client and Graph API.
Unique: Implements full MCP server lifecycle including tool registration, request routing, and OAuth token management, providing a complete bridge between MCP clients and Graph API without requiring custom protocol implementation
vs alternatives: Eliminates need to build custom MCP server from scratch; provides pre-built tool definitions and Graph API integration patterns that would otherwise require significant engineering effort
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 @softeria/ms-365-mcp-server at 26/100. @softeria/ms-365-mcp-server 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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