{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-inbox-zero","slug":"inbox-zero","name":"Inbox Zero","type":"mcp","url":"https://github.com/elie222/inbox-zero/tree/main/apps/mcp-server","page_url":"https://unfragile.ai/inbox-zero","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-inbox-zero__cap_0","uri":"capability://tool.use.integration.mcp.based.email.context.retrieval.and.semantic.search","name":"mcp-based email context retrieval and semantic search","description":"Exposes email data through the Model Context Protocol (MCP) standard, allowing Claude and other LLM clients to query, search, and retrieve email messages using semantic search and structured filtering. Implements MCP resource handlers that translate email queries into database lookups, enabling LLMs to access email context without direct API integration or authentication management.","intents":["Query email history and retrieve specific messages within an LLM conversation","Search emails semantically to find relevant context for AI-assisted email composition","Access email metadata (sender, date, labels) for decision-making in agentic workflows","Build email-aware AI agents that can reason over message history"],"best_for":["AI developers building Claude-based email assistants","Teams integrating email context into LLM-powered workflows","Builders creating multi-tool AI agents that need email awareness"],"limitations":["Requires Inbox Zero backend to be running and accessible","Search performance depends on underlying email database indexing strategy","MCP protocol overhead adds latency compared to direct API calls","No built-in pagination — large result sets may exceed context window limits"],"requires":["Inbox Zero backend instance deployed and configured","MCP client implementation (Claude Desktop, custom MCP runner, or compatible LLM interface)","Email data already ingested into Inbox Zero database","Network connectivity between MCP client and server"],"input_types":["text (search queries)","structured filters (sender, date range, labels)","semantic embeddings (if using vector search)"],"output_types":["structured email objects (JSON with sender, subject, body, date, labels)","search result rankings","metadata summaries"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-inbox-zero__cap_1","uri":"capability://tool.use.integration.email.action.execution.through.mcp.tools","name":"email action execution through mcp tools","description":"Exposes email operations (send, archive, delete, label, snooze) as MCP tool definitions that LLMs can invoke directly. The server implements tool handlers that validate action parameters, apply business logic (e.g., prevent accidental mass deletion), and execute changes against the email backend, enabling AI agents to take autonomous email management actions.","intents":["Allow Claude to send emails on behalf of the user based on conversation context","Enable AI agents to archive, delete, or label emails as part of automated workflows","Implement snooze/defer logic for email triage without manual UI interaction","Build email automation rules that execute through natural language commands"],"best_for":["Developers building autonomous email management agents","Teams automating email triage and organization workflows","Users wanting natural language control over email operations"],"limitations":["Tool execution is synchronous — no built-in queuing for high-volume operations","No transaction rollback if multi-step email workflows fail partway through","Requires explicit user authorization for each action type (send, delete, etc.)","No audit logging built into MCP layer — relies on Inbox Zero backend logging"],"requires":["Inbox Zero backend with email provider integration (Gmail, Outlook, etc.)","MCP client with tool-calling capability (Claude with tool_use feature)","User authentication and authorization tokens for email provider","Email account write permissions configured in Inbox Zero"],"input_types":["text (email body, recipient addresses)","structured parameters (labels, snooze duration, filter criteria)"],"output_types":["action confirmation (success/failure status)","operation metadata (message ID, timestamp)","error messages with remediation hints"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-inbox-zero__cap_2","uri":"capability://tool.use.integration.multi.provider.email.account.abstraction","name":"multi-provider email account abstraction","description":"Abstracts differences between email providers (Gmail, Outlook, IMAP) behind a unified interface, translating provider-specific APIs and authentication mechanisms into consistent MCP resource and tool definitions. The server handles provider-specific label mappings, rate limiting, and protocol differences transparently, allowing LLM clients to interact with any supported email provider using identical MCP calls.","intents":["Build email agents that work across Gmail, Outlook, and other providers without conditional logic","Migrate email workflows between providers without changing LLM prompts or tool definitions","Support users with multiple email accounts across different providers in a single agent","Normalize email concepts (labels, folders, flags) across provider differences"],"best_for":["Developers building email agents for enterprise users with mixed email providers","Teams needing provider-agnostic email automation","SaaS products offering email features to users with any email provider"],"limitations":["Feature parity limited by least-capable provider (e.g., IMAP lacks some Gmail-specific features)","Provider-specific rate limits still apply — no built-in request batching across providers","Authentication refresh token management adds complexity for long-running agents","Some advanced features (e.g., Gmail's threading model) may not translate cleanly to other providers"],"requires":["Inbox Zero backend with provider SDKs/libraries installed (google-auth-library, microsoft-graph-client, etc.)","OAuth credentials or IMAP credentials configured for each email provider","Provider-specific API keys or app passwords stored securely","Network access to provider APIs (Gmail API, Microsoft Graph, IMAP servers)"],"input_types":["provider identifier (gmail, outlook, imap)","account credentials or tokens","unified email operation parameters"],"output_types":["normalized email objects","provider-agnostic operation results","error messages translated to common error types"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-inbox-zero__cap_3","uri":"capability://data.processing.analysis.email.content.parsing.and.structured.extraction","name":"email content parsing and structured extraction","description":"Parses raw email messages (MIME format, HTML, plain text) into structured data, extracting sender, recipient, subject, body, attachments, and metadata. Implements HTML-to-text conversion, MIME decoding, and optional NLP-based entity extraction (dates, action items, decision points) to make email content machine-readable for LLM analysis and decision-making.","intents":["Extract actionable items and deadlines from email conversations for task management","Parse email threads to identify decision points and required responses","Convert HTML emails to clean text for LLM analysis without formatting noise","Extract structured data (invoice numbers, order IDs, dates) from unstructured email bodies"],"best_for":["Developers building email summarization and triage agents","Teams extracting structured data from email-based workflows","Builders creating email-to-task or email-to-CRM automation"],"limitations":["HTML parsing may fail on malformed or unusual email formatting","Entity extraction accuracy depends on NLP model quality — may miss domain-specific entities","Large attachments are not fully parsed — only metadata extracted","Encoding issues with non-UTF-8 emails may require fallback handling"],"requires":["Email messages in MIME format or accessible via email provider API","HTML parsing library (e.g., BeautifulSoup, jsdom equivalent)","Optional: NLP model for entity extraction (spaCy, transformers, or LLM-based)","Character encoding detection library for non-ASCII emails"],"input_types":["raw email (MIME format)","email objects from provider APIs","HTML email bodies"],"output_types":["structured email objects (sender, recipients, subject, body, date)","extracted entities (dates, action items, decision points)","attachment metadata (filename, size, MIME type)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-inbox-zero__cap_4","uri":"capability://data.processing.analysis.email.conversation.threading.and.context.aggregation","name":"email conversation threading and context aggregation","description":"Reconstructs email conversation threads by linking related messages (via In-Reply-To, References headers, and subject matching), then aggregates thread context into a single coherent narrative. Implements thread reconstruction logic that handles provider-specific threading models (Gmail's conversation model vs. traditional IMAP threading) and presents full context to LLMs for holistic conversation understanding.","intents":["Provide LLMs with full email conversation context for intelligent reply suggestions","Summarize multi-message threads into actionable summaries for email triage","Identify conversation participants and their roles across a thread","Detect when a conversation requires escalation or external input"],"best_for":["Developers building email reply suggestion and composition agents","Teams automating email triage and prioritization","Builders creating email summarization tools"],"limitations":["Threading accuracy depends on email provider's threading model — may miss related messages with non-standard headers","Large threads (100+ messages) may exceed LLM context windows","Quoted text in replies can create parsing ambiguity — may include duplicate content","Provider-specific threading (Gmail conversations) may not align with traditional IMAP threading"],"requires":["Email messages with standard headers (In-Reply-To, References, Message-ID)","Access to full email history for a conversation","Provider-specific threading logic if using Gmail or similar conversation-based models","Optional: NLP model for quote detection and removal"],"input_types":["email messages with headers","provider-specific conversation/thread identifiers"],"output_types":["threaded conversation objects (ordered messages with parent-child relationships)","aggregated thread summaries","participant lists with message counts"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-inbox-zero__cap_5","uri":"capability://automation.workflow.email.filtering.and.rule.based.categorization","name":"email filtering and rule-based categorization","description":"Implements rule-based email filtering using criteria (sender, subject patterns, content keywords, labels) to categorize and organize emails automatically. Rules are defined declaratively and executed server-side, applying labels, moving messages to folders, or marking as read based on matching conditions. Integrates with LLM decision-making to suggest or execute rules based on conversation context.","intents":["Create email filters programmatically based on user preferences or LLM suggestions","Automatically categorize incoming emails into folders or labels without manual setup","Implement smart inbox rules that adapt based on user behavior patterns","Build email triage workflows that route messages to appropriate handlers"],"best_for":["Developers building email automation and triage agents","Teams implementing smart inbox features","Users wanting rule-based email organization without manual configuration"],"limitations":["Rule evaluation is sequential — no optimization for complex rule sets","No built-in machine learning for rule suggestion — relies on explicit user/LLM input","Rule conflicts (multiple rules matching same message) require precedence logic","Performance degrades with large rule sets (100+ rules) on high-volume inboxes"],"requires":["Rule definition schema (criteria, actions, precedence)","Email filtering engine (can be simple regex/pattern matching or more complex)","Persistent storage for rule definitions","Email provider support for labels/folders and read status"],"input_types":["rule definitions (criteria, actions)","email messages to filter","user preferences or LLM-suggested rules"],"output_types":["filtered email categorizations","rule execution logs","suggested rules based on patterns"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-inbox-zero__cap_6","uri":"capability://planning.reasoning.email.priority.and.importance.scoring","name":"email priority and importance scoring","description":"Assigns priority or importance scores to emails using heuristics (sender reputation, subject keywords, recipient list size, response time expectations) and optional ML models. Scores are computed server-side and exposed via MCP, allowing LLMs to reason about email importance for triage, response prioritization, and inbox management decisions. Integrates with user feedback to refine scoring over time.","intents":["Help LLMs identify urgent or high-priority emails for immediate attention","Rank emails for response prioritization in triage workflows","Implement smart inbox features that surface important messages","Suggest which emails require human review vs. automated handling"],"best_for":["Developers building email triage and prioritization agents","Teams implementing smart inbox features","Builders creating email summarization tools that focus on important messages"],"limitations":["Scoring heuristics may not align with user priorities — requires personalization","ML models require training data and ongoing refinement","Scoring latency adds overhead if computed on-demand for large inboxes","No built-in feedback loop — requires external mechanism to learn from user actions"],"requires":["Email metadata (sender, subject, recipients, timestamps)","Optional: ML model for importance prediction (trained on user data or general corpus)","Heuristic rules (sender whitelist/blacklist, keyword patterns)","Optional: User feedback mechanism to refine scores"],"input_types":["email objects with metadata","user preferences or feedback","historical email data for model training"],"output_types":["importance scores (numeric or categorical)","reasoning for score (which factors contributed)","confidence levels"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-inbox-zero__cap_7","uri":"capability://text.generation.language.email.draft.composition.and.suggestion","name":"email draft composition and suggestion","description":"Generates email draft suggestions based on conversation context, recipient information, and user preferences. Uses LLM capabilities (via Claude or other models) to compose natural-language email responses, subject lines, and full messages. Integrates with email context retrieval to ensure drafts reference previous conversation history and maintain tone/style consistency.","intents":["Generate intelligent email reply suggestions based on incoming message context","Compose new emails from natural language descriptions or conversation context","Suggest subject lines and email structure for complex messages","Maintain consistent tone and style across email conversations"],"best_for":["Developers building email composition assistants","Teams automating email response workflows","Users wanting AI-assisted email writing without full automation"],"limitations":["Generated drafts require human review before sending — no autonomous sending without explicit approval","LLM hallucination risk if context is incomplete or ambiguous","Tone and style consistency depends on training data and user feedback","No built-in fact-checking — generated content may include inaccuracies"],"requires":["LLM access (Claude via Anthropic API, or other model)","Email context retrieval (previous messages, conversation history)","User preferences or style guide for tone/voice","Optional: User feedback mechanism to refine suggestions"],"input_types":["incoming email or conversation context","user instructions or natural language request","recipient information and history"],"output_types":["email draft (full message)","subject line suggestions","alternative phrasings or tone variations","confidence scores for suggestions"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Inbox Zero backend instance deployed and configured","MCP client implementation (Claude Desktop, custom MCP runner, or compatible LLM interface)","Email data already ingested into Inbox Zero database","Network connectivity between MCP client and server","Inbox Zero backend with email provider integration (Gmail, Outlook, etc.)","MCP client with tool-calling capability (Claude with tool_use feature)","User authentication and authorization tokens for email provider","Email account write permissions configured in Inbox Zero","Inbox Zero backend with provider SDKs/libraries installed (google-auth-library, microsoft-graph-client, etc.)","OAuth credentials or IMAP credentials configured for each email provider"],"failure_modes":["Requires Inbox Zero backend to be running and accessible","Search performance depends on underlying email database indexing strategy","MCP protocol overhead adds latency compared to direct API calls","No built-in pagination — large result sets may exceed context window limits","Tool execution is synchronous — no built-in queuing for high-volume operations","No transaction rollback if multi-step email workflows fail partway through","Requires explicit user authorization for each action type (send, delete, etc.)","No audit logging built into MCP layer — relies on Inbox Zero backend logging","Feature parity limited by least-capable provider (e.g., IMAP lacks some Gmail-specific features)","Provider-specific rate limits still apply — no built-in request batching across providers","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:03.042Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=inbox-zero","compare_url":"https://unfragile.ai/compare?artifact=inbox-zero"}},"signature":"zjA5US/kXm/CE0ZGVX8ERt3rOF+tNcZKd2lyPipBhW/VxxD80OWzkyLh4NDkkbd+55pviMRzutr4IZk0BfbdCA==","signedAt":"2026-06-20T22:52:07.399Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/inbox-zero","artifact":"https://unfragile.ai/inbox-zero","verify":"https://unfragile.ai/api/v1/verify?slug=inbox-zero","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"}}