{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-anyproto-anytype-mcp","slug":"mcp-anyproto-anytype-mcp","name":"anytype-mcp","type":"mcp","url":"https://github.com/anyproto/anytype-mcp","page_url":"https://unfragile.ai/mcp-anyproto-anytype-mcp","categories":["mcp-servers"],"tags":["anytype","api","mcp","mcp-server","modelcontextprotocol"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-anyproto-anytype-mcp__cap_0","uri":"capability://tool.use.integration.openapi.to.mcp.dynamic.tool.conversion","name":"openapi-to-mcp dynamic tool conversion","description":"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.","intents":["I want AI assistants to automatically discover and use all available Anytype API endpoints without manual tool registration","I need tool definitions to stay synchronized with API changes without requiring server redeployment","I want to reduce boilerplate code for converting REST API specs into MCP tool schemas"],"best_for":["MCP server developers integrating third-party REST APIs","Teams maintaining APIs that evolve frequently and need dynamic tool exposure","Builders creating AI assistants that need broad API coverage without manual schema maintenance"],"limitations":["Conversion fidelity depends on OpenAPI spec quality — malformed or incomplete specs produce unusable tool definitions","Complex OpenAPI features (discriminators, polymorphism, circular references) may not convert cleanly to MCP schemas","No caching of converted definitions — conversion happens on every server startup, adding ~500ms overhead for large specs"],"requires":["TypeScript 4.5+","Valid OpenAPI 3.0+ specification from Anytype API","@modelcontextprotocol/sdk package","openapi-client-axios dependency"],"input_types":["OpenAPI 3.0+ JSON/YAML specification"],"output_types":["MCP Tool definitions (JSON schema format)","Executable tool handlers mapped to API endpoints"],"categories":["tool-use-integration","api-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-anyproto-anytype-mcp__cap_1","uri":"capability://tool.use.integration.mcp.protocol.request.handling.and.tool.execution","name":"mcp protocol request handling and tool execution","description":"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.","intents":["I want to expose Anytype API functionality through the MCP protocol so Claude and other AI assistants can use it","I need proper error handling and response formatting that conforms to MCP specification","I want AI assistants to discover available tools and understand their parameters through MCP tool definitions"],"best_for":["AI assistant developers integrating Anytype as a knowledge management backend","Teams building multi-tool AI workflows where Anytype is one of several integrated services","Developers creating MCP servers who need a reference implementation of protocol handling"],"limitations":["MCP protocol overhead adds ~50-100ms per request due to JSON serialization and deserialization","No built-in request batching — each tool call requires a separate HTTP round-trip to Anytype API","Error responses from Anytype API are mapped to generic MCP error format, potentially losing API-specific error context"],"requires":["Node.js 18+","@modelcontextprotocol/sdk package","Valid Anytype API key configured as environment variable","Anytype API endpoint accessibility (HTTP/HTTPS)"],"input_types":["MCP protocol messages (JSON-RPC 2.0 format)","Tool execution requests with typed parameters"],"output_types":["MCP protocol responses (JSON-RPC 2.0 format)","Tool execution results as structured JSON","Error responses with MCP-compliant error codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-anyproto-anytype-mcp__cap_10","uri":"capability://data.processing.analysis.batch.object.operations.and.bulk.updates","name":"batch object operations and bulk updates","description":"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.","intents":["I want AI to update properties on many objects at once without making individual API calls","I need AI to apply tags or relationships to multiple objects based on search results","I want efficient bulk operations that minimize API round-trips"],"best_for":["Teams performing data migrations or bulk updates in Anytype","AI agents that need to coordinate changes across many objects","Builders creating workflows that transform or enrich large datasets"],"limitations":["Batch operations are limited to same-type updates — cannot mix different operations in a single batch","No transaction semantics — if a batch operation fails partway through, some objects may be updated while others are not","Batch size limits may apply — very large batches (10,000+ objects) may need to be split into multiple requests","No rollback capability — failed batch operations require manual correction"],"requires":["Valid Anytype API key with write permissions","Multiple object IDs to operate on","Consistent operation type across all objects"],"input_types":["Array of object IDs","Operation type (update property, add tag, etc.)","Operation parameters (new value, tag name, etc.)"],"output_types":["Count of successfully updated objects","List of failed object IDs (if any)","Summary of changes applied"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-anyproto-anytype-mcp__cap_11","uri":"capability://safety.moderation.encrypted.local.first.data.access.with.cloud.sync","name":"encrypted local-first data access with cloud sync","description":"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.","intents":["I want to use AI assistants with my Anytype knowledge base without exposing unencrypted data to external services","I need assurance that my data remains encrypted at rest and in transit","I want to maintain local-first data ownership while enabling AI integration"],"best_for":["Privacy-conscious users who want AI assistance without cloud data exposure","Organizations with data residency or encryption requirements","Teams using Anytype specifically for its local-first, encrypted architecture"],"limitations":["Encryption keys are managed by Anytype Desktop — the MCP server cannot access or manage keys directly","API key authentication is separate from data encryption — API keys must be protected as secrets","Decryption happens locally in Anytype, not in the MCP server — the server receives plaintext data after local decryption","Cloud sync is optional but recommended — offline-only mode limits AI assistant capabilities to local data only"],"requires":["Anytype Desktop application running locally (for key management and encryption)","Valid Anytype API key (separate from encryption keys)","Local network connectivity to Anytype Desktop"],"input_types":["API requests (which trigger local decryption in Anytype)"],"output_types":["Decrypted object data (plaintext after local decryption)","Encrypted data at rest (in Anytype's local database)"],"categories":["safety-moderation","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-anyproto-anytype-mcp__cap_2","uri":"capability://tool.use.integration.authenticated.http.client.with.anytype.api.integration","name":"authenticated http client with anytype api integration","description":"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.","intents":["I need to securely authenticate with the Anytype API using API keys without hardcoding credentials","I want consistent request/response handling across all API calls with proper error propagation","I need to manage HTTP connection pooling and timeouts for reliable API communication"],"best_for":["Developers building integrations with Anytype's REST API","Teams requiring secure, auditable API key management for knowledge management systems","MCP server builders who need a reference HTTP client implementation with proper auth"],"limitations":["API key must be stored in environment variables — no built-in key rotation or expiration handling","No request retry logic — transient network failures cause immediate failure without backoff","HTTP client does not implement request signing or mutual TLS, relying on HTTPS + API key only","No request/response logging by default — debugging API issues requires external instrumentation"],"requires":["Anytype API key (obtained via Anytype Desktop or CLI tool)","ANYTYPE_API_KEY environment variable configured","axios package (HTTP client library)","Network connectivity to Anytype API endpoint"],"input_types":["HTTP method (GET, POST, PUT, DELETE, etc.)","API endpoint path","Request body (JSON)","Query parameters"],"output_types":["HTTP response body (JSON)","HTTP status codes","Error objects with API error details"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-anyproto-anytype-mcp__cap_3","uri":"capability://automation.workflow.cli.based.api.key.generation.and.server.startup","name":"cli-based api key generation and server startup","description":"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.","intents":["I want a simple way to generate and configure Anytype API keys without manual environment variable editing","I need to start the MCP server with a single command that handles all initialization","I want interactive guidance for first-time setup rather than reading documentation"],"best_for":["Non-technical users setting up Anytype integration for the first time","DevOps teams automating MCP server deployment in containers or CI/CD pipelines","Developers prototyping AI assistant integrations who want quick setup"],"limitations":["CLI requires interactive terminal input for auth setup — cannot be fully automated without pre-configured environment variables","No built-in support for multiple API keys or key rotation — one key per environment variable","Server startup binds to stdio only — no built-in HTTP server or socket support for alternative transport mechanisms","No configuration file support — all settings must be environment variables or CLI flags"],"requires":["Node.js 18+","npm or yarn package manager","Anytype Desktop application installed (for API key generation)","Terminal/shell access"],"input_types":["CLI command arguments (auth, start)","Interactive user input (API key, environment variable names)","Environment variables (ANYTYPE_API_KEY)"],"output_types":["Formatted console output with setup instructions","MCP server stdio stream (for AI assistant communication)","Exit codes indicating success/failure"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-anyproto-anytype-mcp__cap_4","uri":"capability://search.retrieval.global.and.space.scoped.full.text.search","name":"global and space-scoped full-text search","description":"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.","intents":["I want AI assistants to find relevant information in my Anytype knowledge base using natural language queries","I need to search within specific spaces to avoid irrelevant results from other workspaces","I want search results to include metadata (object type, tags, properties) so AI can reason about relevance"],"best_for":["Knowledge workers using Anytype as a personal or team wiki who want AI-powered search","AI agents that need to retrieve context before generating responses or making decisions","Teams building RAG (Retrieval-Augmented Generation) systems on top of Anytype"],"limitations":["Search results are limited to indexed content — newly created objects may not appear in results immediately","Full-text search does not support advanced query syntax (boolean operators, phrase matching) — only simple keyword matching","Search performance degrades with large knowledge bases (10,000+ objects) — no pagination or result limiting built-in","Search results do not include object content/body text — only metadata and titles, requiring separate fetch calls for full content"],"requires":["Valid Anytype API key with search permissions","At least one space created in Anytype","Objects indexed in Anytype (automatic after creation)"],"input_types":["Search query string (natural language or keywords)","Space ID (optional, for space-scoped search)","Filter parameters (object type, tags, properties)"],"output_types":["Array of search results with object metadata","Result ranking/relevance scores","Object IDs, titles, types, and tags"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-anyproto-anytype-mcp__cap_5","uri":"capability://tool.use.integration.object.creation.and.manipulation.with.type.template.support","name":"object creation and manipulation with type/template support","description":"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.","intents":["I want AI to create new documents, tasks, or other object types in my Anytype workspace based on natural language requests","I need AI to populate object properties (title, description, tags, custom fields) when creating objects","I want AI to organize objects into lists and establish relationships between objects"],"best_for":["Knowledge workers who want AI to help organize and structure their Anytype workspace","Teams using Anytype as a project management or CRM system where AI assists with data entry","Builders creating AI agents that need to persist structured information in a knowledge base"],"limitations":["Object creation requires knowing the correct type IDs and property names — no built-in type discovery or validation","Complex nested properties or relationships may require multiple API calls rather than single atomic creation","No transaction support — if multi-step object creation fails partway through, manual cleanup may be required","Template application is limited to predefined templates in Anytype — cannot create custom templates via API"],"requires":["Valid Anytype API key with write permissions","Target space ID where objects will be created","Knowledge of object type IDs and property schemas (available via API introspection)"],"input_types":["Object type (Document, Task, Person, etc.)","Object properties (title, description, custom fields)","Template ID (optional)","Relationship targets (links to other objects)"],"output_types":["Created object ID","Object metadata (type, creation timestamp, creator)","Confirmation of property assignments"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-anyproto-anytype-mcp__cap_6","uri":"capability://tool.use.integration.space.and.member.management","name":"space and member management","description":"Exposes Anytype's space and collaboration management APIs through MCP, allowing AI assistants to create spaces, invite members, manage permissions, and configure space settings. Spaces are Anytype's unit of collaboration and encryption — each space is independently encrypted and can have different members with different permission levels.","intents":["I want AI to help set up new collaborative spaces in Anytype for different teams or projects","I need AI to manage space membership and permissions without manual admin work","I want to automate space configuration (naming, description, settings) through AI instructions"],"best_for":["Team leads and administrators managing multiple Anytype spaces","Organizations automating workspace provisioning for new teams or projects","Builders creating AI agents that manage collaborative knowledge bases"],"limitations":["Space creation and member management require admin-level API permissions — cannot be delegated to regular users","Permission model is binary (member/non-member) — no fine-grained role-based access control via API","No bulk member operations — adding multiple members requires separate API calls per member","Space deletion is not supported via API — spaces must be deleted manually through Anytype Desktop"],"requires":["Valid Anytype API key with admin permissions","Anytype account with space creation privileges"],"input_types":["Space name and description","Member email addresses or user IDs","Permission levels (member, admin, etc.)"],"output_types":["Created space ID","Space metadata (name, creation timestamp, member list)","Confirmation of member invitations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-anyproto-anytype-mcp__cap_7","uri":"capability://data.processing.analysis.property.and.tag.management","name":"property and tag management","description":"Allows AI assistants to manage object properties (custom fields, metadata) and tags through MCP tools. Properties can be of various types (text, number, date, relation, etc.) and are defined at the object type level. Tags provide a lightweight categorization mechanism independent of the type system, enabling flexible organization without schema changes.","intents":["I want AI to add or modify custom properties on objects to capture additional metadata","I need AI to apply tags to objects for flexible categorization and filtering","I want to bulk-update properties across multiple objects based on AI reasoning"],"best_for":["Knowledge workers who want AI to help organize and enrich object metadata","Teams using Anytype for structured data management (CRM, project tracking) where AI assists with data quality","Builders creating AI workflows that need to annotate or categorize information"],"limitations":["Property types are defined at the object type level — cannot add arbitrary properties to individual objects","Property updates require knowing the exact property ID and type — no schema discovery in the tool interface","Tag operations are limited to adding/removing tags — no bulk tag operations or tag hierarchy support","No validation of property values — invalid values may be accepted by the API but cause issues in Anytype Desktop"],"requires":["Valid Anytype API key with write permissions","Object ID to modify","Property or tag names/IDs"],"input_types":["Object ID","Property name and value","Tag names"],"output_types":["Updated object metadata","Confirmation of property/tag changes"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-anyproto-anytype-mcp__cap_8","uri":"capability://memory.knowledge.type.and.template.discovery.and.application","name":"type and template discovery and application","description":"Provides AI assistants with access to Anytype's type system and template library through MCP tools. AI can discover available object types (Document, Task, Person, etc.), their properties and constraints, and available templates. When creating objects, AI can apply templates to inherit predefined properties and structure, ensuring consistency across the workspace.","intents":["I want AI to understand what object types are available in my workspace and their properties","I need AI to apply templates when creating objects to maintain consistent structure","I want AI to discover custom types and templates defined in my workspace"],"best_for":["Teams with custom object types and templates who want AI to respect workspace conventions","Knowledge workers who want AI to understand their workspace structure before creating objects","Builders creating AI agents that need to generate objects conforming to workspace schemas"],"limitations":["Type and template discovery is read-only — cannot create new types or templates via API","Template application is limited to predefined templates — cannot dynamically generate templates","Type constraints and validation rules are not fully exposed via API — AI must infer constraints from property metadata","No type hierarchy or inheritance — each type is independent with no parent-child relationships"],"requires":["Valid Anytype API key with read permissions","Space ID to query types and templates from"],"input_types":["Space ID (to scope type/template discovery)"],"output_types":["List of available object types with properties","List of available templates with descriptions","Property schemas (type, constraints, required/optional)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-anyproto-anytype-mcp__cap_9","uri":"capability://data.processing.analysis.list.and.relation.management","name":"list and relation management","description":"Enables AI assistants to create and manage lists (collections of objects) and establish relationships (links) between objects. Lists are filtered views of objects matching certain criteria, while relations are explicit links between objects that can be traversed. AI can create lists based on object type or properties, add/remove objects from lists, and establish bidirectional relationships.","intents":["I want AI to organize objects into lists for easier browsing and filtering","I need AI to establish relationships between objects (e.g., linking a task to a project)","I want AI to create dynamic lists that automatically include objects matching certain criteria"],"best_for":["Teams using Anytype for project management or knowledge organization who want AI to structure information","Knowledge workers who want AI to establish connections between related objects","Builders creating AI agents that need to organize and relate information"],"limitations":["List creation requires specifying filter criteria — cannot create arbitrary lists without filters","Relationships are directional but can be bidirectional — no support for typed relationships with semantic meaning","List membership is dynamic based on filters — cannot manually add/remove objects from filtered lists","No list hierarchy or nesting — lists are flat collections at the space level"],"requires":["Valid Anytype API key with write permissions","Space ID where lists will be created","Object IDs for establishing relationships"],"input_types":["List name and filter criteria","Source and target object IDs (for relationships)","Relationship type/label"],"output_types":["Created list ID","List metadata (name, filter criteria, member count)","Confirmation of relationship creation"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["TypeScript 4.5+","Valid OpenAPI 3.0+ specification from Anytype API","@modelcontextprotocol/sdk package","openapi-client-axios dependency","Node.js 18+","Valid Anytype API key configured as environment variable","Anytype API endpoint accessibility (HTTP/HTTPS)","Valid Anytype API key with write permissions","Multiple object IDs to operate on","Consistent operation type across all objects"],"failure_modes":["Conversion fidelity depends on OpenAPI spec quality — malformed or incomplete specs produce unusable tool definitions","Complex OpenAPI features (discriminators, polymorphism, circular references) may not convert cleanly to MCP schemas","No caching of converted definitions — conversion happens on every server startup, adding ~500ms overhead for large specs","MCP protocol overhead adds ~50-100ms per request due to JSON serialization and deserialization","No built-in request batching — each tool call requires a separate HTTP round-trip to Anytype API","Error responses from Anytype API are mapped to generic MCP error format, potentially losing API-specific error context","Batch operations are limited to same-type updates — cannot mix different operations in a single batch","No transaction semantics — if a batch operation fails partway through, some objects may be updated while others are not","Batch size limits may apply — very large batches (10,000+ objects) may need to be split into multiple requests","No rollback capability — failed batch operations require manual correction","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.33192673879188833,"quality":0.49,"ecosystem":0.55,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T14:23:38.364Z","last_commit":"2026-04-29T22:35:19Z"},"community":{"stars":402,"forks":44,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-anyproto-anytype-mcp","compare_url":"https://unfragile.ai/compare?artifact=mcp-anyproto-anytype-mcp"}},"signature":"IlQxCuDU/DCHdfxfTfGk3Hi9CU+2y1+nwWCoL+Uf01ansjjvLn9xXOLp+u0CcWobu94eaKhvUgbGZisGwBnlDA==","signedAt":"2026-06-22T09:58:30.616Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-anyproto-anytype-mcp","artifact":"https://unfragile.ai/mcp-anyproto-anytype-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-anyproto-anytype-mcp","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}