| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Automatically transforms Anytype's OpenAPI specification into MCP tool definitions at runtime using the OpenAPIToMCPConverter component. This eliminates manual tool definition maintenance by dynamically generating tool schemas, descriptions, and parameter mappings from the source OpenAPI spec, ensuring AI assistants always have access to the latest API endpoints without code changes.
Unique: Uses openapi-client-axios to parse OpenAPI specs and dynamically generate both tool schemas AND executable handlers in a single pass, rather than requiring separate schema definition and implementation files. The MCPProxy layer then wraps these generated handlers with MCP protocol semantics.
vs alternatives: Eliminates the manual tool definition burden that plagues most MCP servers (which hardcode tools), enabling instant API coverage expansion as Anytype's API evolves without code changes.
The MCPProxy component implements the MCP protocol specification, handling incoming tool listing requests and tool execution calls from AI assistants. It translates MCP-formatted requests into HTTP calls to the Anytype API via the HttpClient layer, manages response serialization back to MCP format, and handles protocol-level error mapping to ensure AI assistants receive properly formatted results.
Unique: Implements a two-layer protocol translation: MCP → internal tool representation → HTTP REST calls, with explicit error mapping at each layer. The MCPProxy maintains state about available tools (from the OpenAPI converter) and validates incoming requests against generated schemas before forwarding to the HTTP client.
vs alternatives: Provides complete MCP protocol compliance with proper tool discovery and execution semantics, whereas naive REST-to-MCP adapters often skip protocol validation and error handling, leading to fragile AI assistant integrations.
Supports efficient bulk operations on multiple objects through MCP, allowing AI assistants to update properties, apply tags, or modify relationships across many objects in a single workflow. Rather than making individual API calls per object, batch operations reduce latency and improve efficiency when AI needs to perform coordinated changes across the knowledge base.
Unique: Provides batch operation support through MCP, reducing the number of HTTP round-trips required for bulk updates. The implementation groups multiple object updates into single API calls, improving performance compared to sequential individual updates.
vs alternatives: More efficient than sequential individual API calls (which require N round-trips for N objects), but less transactional than database-level batch operations (which provide ACID guarantees).
Anytype's architecture ensures all data is encrypted locally before any network transmission, and the MCP server respects this encryption model. Objects are stored encrypted in Anytype's local database, and when accessed through the API, decryption happens locally before data is returned. The MCP server does not handle encryption/decryption directly — it relies on Anytype's local client to manage keys and encryption, ensuring end-to-end encryption even when accessed through AI assistants.
Unique: Leverages Anytype's local-first encryption architecture where encryption keys never leave the user's device and decryption happens locally before data is exposed to the MCP server. The MCP server acts as a trusted local proxy that respects Anytype's encryption model rather than implementing its own encryption.
vs alternatives: Stronger privacy guarantees than cloud-based knowledge management systems (where data is encrypted in transit but decrypted on servers), but requires local Anytype Desktop running to manage encryption keys.
The HttpClient component manages all HTTP communication with the Anytype REST API, handling request formatting, authentication header injection, response parsing, and connection management. It uses axios for HTTP transport and implements a challenge-response authentication mechanism where API keys (generated via Anytype Desktop or CLI) are injected as Authorization headers on every request.
Unique: Implements a stateless HTTP client that relies on environment variable-based API key injection rather than connection-level authentication, allowing the same client instance to be used across multiple concurrent requests without session management overhead. Uses openapi-client-axios to generate typed API client methods from the OpenAPI spec.
vs alternatives: Simpler than building a custom HTTP client with manual header management, but less flexible than full-featured API client libraries that support advanced features like request signing, certificate pinning, or automatic retry logic.
The command-line interface provides two primary functions: (1) authentication setup via `anytype-mcp auth` which guides users through generating API keys via Anytype Desktop and configuring environment variables, and (2) server startup via `anytype-mcp start` which initializes the MCP server and binds it to stdio for communication with AI assistants. The CLI abstracts away configuration complexity and provides interactive prompts for first-time setup.
Unique: Provides an interactive CLI that guides users through the Anytype Desktop API key generation flow rather than requiring manual key copying, reducing setup friction. The `start` command directly binds the MCP server to stdio, enabling seamless integration with AI assistant platforms that expect stdio-based MCP servers.
vs alternatives: More user-friendly than requiring manual environment variable configuration, but less flexible than configuration file-based approaches that support multiple environments and key rotation strategies.
Exposes Anytype's search API endpoints through MCP tools, enabling AI assistants to perform full-text search across all objects globally or within specific spaces. The search capability supports query parameters for filtering by object type, tags, and properties, returning ranked results with metadata that AI assistants can use to understand context and relationships within the knowledge base.
Unique: Integrates Anytype's native full-text search engine (which indexes all object properties and relationships) through MCP, allowing AI assistants to leverage the same search capabilities that Anytype users have in the desktop client. Supports both global and space-scoped searches, enabling multi-workspace knowledge bases.
vs alternatives: More efficient than embedding-based semantic search for exact keyword matching and metadata filtering, but less flexible for fuzzy or conceptual queries compared to vector similarity search.
Enables AI assistants to create new objects in Anytype with specified types (e.g., Document, Task, Person) and templates, set properties and relationships, and organize objects into lists. The capability maps Anytype's object model (where each object has a type, properties, and relationships) to MCP tool parameters, allowing AI to construct complex knowledge structures through natural language instructions.
Unique: Leverages Anytype's type system and template engine to enable structured object creation with schema validation, rather than generic key-value storage. AI assistants can create objects that conform to workspace-specific types and inherit properties from templates, maintaining data consistency.
vs alternatives: More structured than generic document creation (which would require manual property mapping), but requires upfront schema definition in Anytype compared to schemaless databases.
+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 anytype-mcp at 34/100. anytype-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.
+5 more capabilities