{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_context","slug":"context","name":"Context","type":"product","url":"https://usecontext.io","page_url":"https://unfragile.ai/context","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_context__cap_0","uri":"capability://tool.use.integration.ide.embedded.real.time.support.query.resolution","name":"ide-embedded real-time support query resolution","description":"Embeds an AI-powered support assistant directly within VS Code and other IDEs, intercepting developer questions before they context-switch to external support channels. The system maintains a persistent connection to a knowledge base indexed from company documentation, previous tickets, and FAQs, using semantic search to retrieve relevant answers within milliseconds. Responses are streamed directly into the editor's sidebar or inline, eliminating the need to open Slack, email, or ticketing systems.","intents":["Get instant answers to technical questions without leaving my IDE","Reduce time spent waiting for support team responses on repetitive issues","Access company-specific documentation and troubleshooting guides inline while coding","Avoid context-switching between IDE and external support tools"],"best_for":["Development teams with 10+ engineers experiencing high support ticket volume","SaaS companies with repetitive onboarding or API integration questions","Organizations where developers spend >2 hours/week on support-related context-switching"],"limitations":["Requires pre-indexed knowledge base; cold-start with new companies takes 1-2 weeks of data ingestion","IDE extension adds ~50-100MB to VS Code installation and ~5-10% memory overhead","Accuracy degrades significantly if knowledge base is outdated or poorly structured; requires active curation","No offline mode; requires persistent internet connection and API availability","Limited to VS Code; support for JetBrains IDEs, Vim, or Neovim unknown"],"requires":["VS Code 1.70+ or compatible IDE with extension API","Active internet connection with <500ms latency to Context API","Company knowledge base or documentation in accessible format (Markdown, Confluence, Notion, or GitHub Wiki)","API key provisioned by Context admin dashboard","Minimum 50 historical support tickets or documentation pages for effective semantic indexing"],"input_types":["natural language text queries","code snippets (for context-aware troubleshooting)","error messages and stack traces"],"output_types":["natural language explanations","code examples and snippets","links to documentation or related tickets","structured troubleshooting steps"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_context__cap_1","uri":"capability://tool.use.integration.slack.integrated.support.bot.with.knowledge.base.retrieval","name":"slack-integrated support bot with knowledge base retrieval","description":"Deploys a Slack bot that intercepts support questions posted in team channels or DMs, queries a semantic index of company knowledge bases and previous ticket resolutions, and responds with relevant answers or escalation paths. The bot uses natural language understanding to classify question intent, retrieve top-K similar past resolutions from a vector database, and synthesize responses with citations back to source documentation. Integration with Slack's message threading and reaction APIs allows developers to provide feedback on answer quality, which feeds back into the knowledge base ranking.","intents":["Answer common support questions in Slack without waiting for a human support engineer","Automatically surface relevant documentation or past ticket resolutions when someone asks a question","Reduce support team workload by handling first-response to repetitive questions","Track which questions are most frequently asked and identify knowledge gaps"],"best_for":["Teams already using Slack as primary communication channel","Companies with 20+ engineers where support questions are scattered across multiple channels","Organizations with mature documentation but poor discoverability"],"limitations":["Slack API rate limits (60 requests/minute for standard bots) may cause delays during high-volume question periods","Bot cannot access private channels unless explicitly granted; may miss questions in siloed team spaces","Responses limited to Slack's message formatting (2000 character limit); long-form answers require linking to external docs","No persistent conversation memory across Slack workspace resets or bot re-deployments","Requires manual training data curation; bot quality depends entirely on knowledge base quality and freshness"],"requires":["Slack workspace with admin permissions to install custom apps","Slack API token with scopes: chat:write, reactions:read, users:read","Indexed knowledge base (minimum 100 documents or 500 historical tickets)","Vector database or embedding service (e.g., Pinecone, Weaviate) for semantic search","Webhook endpoint or serverless function (AWS Lambda, Google Cloud Functions) to host bot logic"],"input_types":["natural language text messages in Slack","threaded replies and reactions","user metadata (department, seniority level if available)"],"output_types":["formatted Slack messages with citations","links to documentation or ticket resolutions","escalation recommendations to human support"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_context__cap_2","uri":"capability://memory.knowledge.semantic.knowledge.base.indexing.and.vector.embedding","name":"semantic knowledge base indexing and vector embedding","description":"Automatically ingests company documentation, support tickets, API docs, and FAQs from multiple sources (GitHub, Confluence, Notion, Zendesk, custom databases) and converts them into dense vector embeddings using a multi-lingual embedding model. The system maintains a vector database (likely Pinecone, Weaviate, or Milvus) indexed by semantic similarity, allowing sub-100ms retrieval of top-K most relevant documents for any query. Includes automated deduplication, freshness tracking, and metadata tagging (source, date, confidence score) to ensure retrieved results are current and traceable.","intents":["Index all company documentation and past support tickets into a searchable knowledge base","Retrieve the most relevant documentation or past solutions for any new support question","Automatically keep the knowledge base in sync with documentation updates across multiple platforms","Identify and deduplicate similar questions or solutions to reduce knowledge base bloat"],"best_for":["Companies with documentation spread across 3+ platforms (GitHub, Confluence, Zendesk, etc.)","Organizations with 1000+ historical support tickets that need to be searchable","Teams that update documentation frequently and need real-time indexing"],"limitations":["Embedding quality depends on model choice; generic models may miss domain-specific terminology (e.g., product-specific jargon)","Vector search is approximate, not exact; may miss relevant documents if they use different terminology than the query","Indexing latency varies by source: GitHub/Confluence ~5-10 minutes, Zendesk API ~15-30 minutes, custom databases depends on connector implementation","Storage costs scale with knowledge base size; 10,000 documents at 1536-dim embeddings ≈ $50-200/month in vector DB costs","No built-in handling of multi-language queries; requires separate embedding models per language"],"requires":["API credentials for source systems (GitHub, Confluence, Zendesk, Notion, etc.)","Vector database account (Pinecone, Weaviate, Milvus, or self-hosted alternative)","Embedding model API access (OpenAI, Cohere, or self-hosted Sentence Transformers)","Minimum 50 documents or tickets for meaningful semantic indexing","Scheduled job runner (cron, Airflow, or cloud scheduler) for periodic re-indexing"],"input_types":["markdown and HTML documentation","PDF files","Zendesk ticket transcripts","Confluence pages","GitHub issues and discussions","Notion databases"],"output_types":["vector embeddings (1536-dim or configurable)","metadata-tagged document chunks","relevance scores and similarity rankings","deduplication reports"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_context__cap_3","uri":"capability://tool.use.integration.multi.channel.support.escalation.and.routing","name":"multi-channel support escalation and routing","description":"Automatically detects when an AI-generated response is insufficient or the question requires human expertise, and routes the conversation to the appropriate support team member via Slack, email, or ticketing system. Uses confidence scoring on AI responses (based on embedding similarity, knowledge base coverage, and historical resolution rates) to determine escalation thresholds. Maintains conversation context across channels, so when a developer escalates from IDE to Slack to email, the support engineer sees the full conversation history and previous AI attempts.","intents":["Automatically escalate complex or novel questions to human support without losing context","Route questions to the right team member based on expertise tags or availability","Ensure developers don't get stuck with unhelpful AI responses; provide a clear escalation path","Track which types of questions require human intervention to identify training gaps"],"best_for":["Teams with mixed AI + human support workflows","Companies where some questions are routine (AI-solvable) and others require domain expertise","Organizations that want to measure AI effectiveness by tracking escalation rates"],"limitations":["Escalation routing requires manual configuration of team expertise tags; no automatic skill detection","Confidence scoring is heuristic-based; may escalate too aggressively (false positives) or too conservatively (false negatives)","Context preservation across channels requires custom integrations; not all ticketing systems support rich context import","No built-in SLA tracking or escalation timeout; requires external monitoring to ensure escalated tickets are handled timely","Escalation to email loses real-time interactivity; developers may not see response for hours"],"requires":["Slack workspace with bot permissions, OR email integration with SMTP/IMAP access, OR Zendesk/Jira API key","Support team member directory with expertise tags (e.g., 'API', 'Database', 'Authentication')","Confidence threshold configuration (typically 0.6-0.8 on 0-1 scale)","Conversation history storage (database or log service) to maintain context across channels"],"input_types":["AI response confidence scores","question intent classification","support team availability status","expertise tags"],"output_types":["escalation notifications to Slack or email","ticket creation in external systems","routed conversation with full context","escalation reason and metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_context__cap_4","uri":"capability://tool.use.integration.github.integrated.code.context.for.support.queries","name":"github-integrated code context for support queries","description":"Automatically extracts relevant code context from a developer's GitHub repository (specific files, recent commits, pull requests, issues) when they ask a support question, and includes this context in the knowledge base query to provide more targeted answers. Uses GitHub API to fetch repository metadata, file contents, and commit history, then augments the semantic search with code-specific context (e.g., 'show me how this API is used in our codebase'). Respects GitHub access controls; only surfaces code from repositories the developer has access to.","intents":["Get support answers that reference how our codebase actually uses the API or feature","Automatically include relevant code snippets from our repo in support responses","Find similar issues or solutions from our own GitHub issues and pull requests","Reduce time spent manually copying code context into support tickets"],"best_for":["Development teams using GitHub for version control and issue tracking","Companies where support questions are often about API integration or library usage","Organizations with large codebases where code examples are more helpful than generic docs"],"limitations":["Requires GitHub API token with repo read access; cannot work with private repositories without explicit permissions","Code context extraction adds 500ms-2s latency per query (GitHub API calls + code parsing)","Large repositories (>10,000 files) may exceed GitHub API rate limits during high-volume query periods","Code parsing is language-agnostic but may miss context in dynamically-typed or metaprogramming-heavy code","No support for GitLab, Bitbucket, or other Git platforms; GitHub-only"],"requires":["GitHub account with access to relevant repositories","GitHub personal access token with 'repo' scope (read-only)","Repository must be indexed by Context (requires admin setup)","Minimum 100 commits or 50 files for meaningful code context extraction"],"input_types":["natural language support queries","GitHub repository URLs or names","file paths or function names (optional)"],"output_types":["code snippets from the repository","links to relevant GitHub issues or PRs","usage examples from actual codebase","commit history context"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_context__cap_5","uri":"capability://data.processing.analysis.support.ticket.analytics.and.knowledge.gap.detection","name":"support ticket analytics and knowledge gap detection","description":"Analyzes historical support tickets and AI response logs to identify patterns: which questions are asked most frequently, which have the lowest resolution rates, which require escalation most often, and which topics are missing from the knowledge base. Generates automated reports showing knowledge gaps (e.g., 'API authentication questions have 40% escalation rate; recommend adding 5 new docs'), trending issues, and team performance metrics. Uses clustering algorithms to group similar questions and identify duplicate or near-duplicate tickets that could be consolidated.","intents":["Identify which topics need better documentation to reduce support volume","Track AI support effectiveness by measuring resolution rates and escalation rates","Find patterns in support questions to prioritize documentation improvements","Measure support team performance and identify bottlenecks"],"best_for":["Support teams that want data-driven insights into documentation gaps","Companies measuring ROI of AI support tools","Organizations with 500+ monthly support tickets"],"limitations":["Analytics are retrospective; cannot predict future support volume or trends","Clustering algorithms may group dissimilar questions if they use similar terminology","Requires 100+ tickets for statistically meaningful patterns; small teams may see noisy results","No built-in integration with business metrics (revenue impact, customer churn); requires manual correlation","Reports are generated on fixed schedules (daily/weekly); no real-time alerting for sudden spikes"],"requires":["Minimum 100 historical support tickets or 1 month of AI support logs","Access to ticket database or Context's internal analytics API","Optional: Slack webhook for automated report delivery"],"input_types":["support ticket transcripts","AI response logs with confidence scores","escalation events and reasons","resolution status (resolved, escalated, abandoned)"],"output_types":["analytics dashboards (web UI)","CSV/JSON reports","knowledge gap recommendations","clustering reports showing similar questions","trend analysis and forecasts"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_context__cap_6","uri":"capability://memory.knowledge.custom.knowledge.base.training.and.fine.tuning","name":"custom knowledge base training and fine-tuning","description":"Allows teams to train Context's AI model on company-specific terminology, product features, and support patterns by uploading custom training data (past tickets, documentation, internal wikis, or labeled Q&A pairs). Uses this training data to fine-tune the semantic embeddings and response generation, making the system more accurate for domain-specific questions. Includes active learning: the system flags low-confidence responses and asks support engineers to provide corrections, which are automatically incorporated into the next training cycle.","intents":["Improve AI accuracy on company-specific terminology and product features","Train the system on our unique support patterns and best practices","Continuously improve accuracy by learning from support engineer corrections","Customize responses to match our company's tone and support philosophy"],"best_for":["Companies with highly specialized products or domain-specific terminology","Organizations with mature support processes and documented best practices","Teams willing to invest in training data curation for better long-term accuracy"],"limitations":["Fine-tuning requires 500+ labeled examples for meaningful improvement; smaller datasets may overfit","Training cycles take 24-48 hours; cannot be done in real-time","Active learning requires manual labeling by support engineers; adds overhead if not automated","Fine-tuned models may become less general-purpose; may perform worse on out-of-domain questions","No transparency into what the model learned; difficult to debug why specific responses changed"],"requires":["Minimum 500 labeled Q&A pairs or support tickets","CSV or JSON format with question, answer, and confidence labels","Support team availability for active learning feedback (1-2 hours/week)","Access to Context's training API (may require enterprise plan)"],"input_types":["labeled Q&A pairs (question, correct answer, confidence)","support ticket transcripts with resolutions","company documentation and wikis","feedback from support engineers on AI responses"],"output_types":["fine-tuned embedding model","improved response accuracy metrics","training reports showing what changed"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_context__cap_7","uri":"capability://data.processing.analysis.real.time.support.metrics.dashboard.and.alerting","name":"real-time support metrics dashboard and alerting","description":"Provides a real-time dashboard showing support team performance metrics: average response time (AI vs human), resolution rate, escalation rate, customer satisfaction (if integrated with surveys), and ticket volume trends. Includes configurable alerts for anomalies (e.g., 'escalation rate jumped to 60% in the last hour') and SLA tracking (e.g., 'human support response time exceeded 2 hours'). Integrates with Slack to send alerts to support channels, allowing teams to react quickly to support bottlenecks.","intents":["Monitor support team performance in real-time","Get alerted when support metrics degrade (e.g., high escalation rate)","Track SLA compliance and identify when human support is overloaded","Measure the impact of documentation improvements on support volume"],"best_for":["Support teams with 5+ members and defined SLAs","Companies that want to measure AI support effectiveness","Organizations with high-volume support where real-time monitoring is critical"],"limitations":["Metrics are only as good as the data; requires accurate ticket categorization and resolution status","Real-time dashboards add operational overhead; teams must monitor and respond to alerts","SLA tracking requires manual configuration per team; no automatic SLA detection","No predictive analytics; cannot forecast future support volume or staffing needs","Alerts may be noisy if thresholds are not tuned correctly; requires iteration to find right sensitivity"],"requires":["Active support tickets in Context system (minimum 50/month for meaningful metrics)","Slack workspace for alert delivery (optional but recommended)","Configured SLA thresholds and escalation rules","Support team member assignments for workload tracking"],"input_types":["support ticket events (created, resolved, escalated)","AI response logs with confidence scores","human support response times","customer satisfaction surveys (if integrated)"],"output_types":["real-time dashboard (web UI)","Slack alerts","CSV/JSON exports of metrics","trend analysis and forecasts"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.70+ or compatible IDE with extension API","Active internet connection with <500ms latency to Context API","Company knowledge base or documentation in accessible format (Markdown, Confluence, Notion, or GitHub Wiki)","API key provisioned by Context admin dashboard","Minimum 50 historical support tickets or documentation pages for effective semantic indexing","Slack workspace with admin permissions to install custom apps","Slack API token with scopes: chat:write, reactions:read, users:read","Indexed knowledge base (minimum 100 documents or 500 historical tickets)","Vector database or embedding service (e.g., Pinecone, Weaviate) for semantic search","Webhook endpoint or serverless function (AWS Lambda, Google Cloud Functions) to host bot logic"],"failure_modes":["Requires pre-indexed knowledge base; cold-start with new companies takes 1-2 weeks of data ingestion","IDE extension adds ~50-100MB to VS Code installation and ~5-10% memory overhead","Accuracy degrades significantly if knowledge base is outdated or poorly structured; requires active curation","No offline mode; requires persistent internet connection and API availability","Limited to VS Code; support for JetBrains IDEs, Vim, or Neovim unknown","Slack API rate limits (60 requests/minute for standard bots) may cause delays during high-volume question periods","Bot cannot access private channels unless explicitly granted; may miss questions in siloed team spaces","Responses limited to Slack's message formatting (2000 character limit); long-form answers require linking to external docs","No persistent conversation memory across Slack workspace resets or bot re-deployments","Requires manual training data curation; bot quality depends entirely on knowledge base quality and freshness","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.281Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=context","compare_url":"https://unfragile.ai/compare?artifact=context"}},"signature":"F3qEplr/jvRtoIjf3TVvyS10WWAovDWBKfBjThsejcSzjsHCQAVW1ntz6YUEs1xeuTh99FSdOU4QoibdKqOvAw==","signedAt":"2026-06-21T22:03:15.521Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/context","artifact":"https://unfragile.ai/context","verify":"https://unfragile.ai/api/v1/verify?slug=context","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"}}