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Uses a document embedding and retrieval pattern to make codebase knowledge queryable by agents without requiring them to parse source files directly.","intents":["I want my agent to index a codebase and generate documentation automatically","I need agents to query documentation to understand API contracts and code structure","I'm building a code assistant that needs semantic search over documentation"],"best_for":["AI code assistants that need codebase context without full source parsing","Documentation automation workflows driven by agents","Teams building internal knowledge bases from code repositories"],"limitations":["Indexing large codebases may exceed MCP message size limits — requires batching or streaming","Documentation quality depends on source code quality and comment coverage","No real-time sync — agents see stale documentation until re-indexing is triggered"],"requires":["Source code repository access (local or remote)","AgentDocs service or compatible documentation backend","Sufficient storage for documentation indices"],"input_types":["repository paths or git URLs","documentation query strings","code file contents"],"output_types":["documentation artifacts (markdown, structured metadata)","search results with relevance scores","indexing status and progress"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-opvs-aimcp__cap_3","uri":"capability://memory.knowledge.agentmemory.persistent.context.management","name":"agentmemory-persistent-context-management","description":"Exposes AgentMemory's persistent context storage and retrieval as MCP tools, enabling agents to save and recall conversation history, learned facts, and execution traces across sessions. 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Provides tools for agent-to-agent messaging, capability discovery, and protocol-compliant request/response handling, allowing agents to interoperate within the OPVS ecosystem without custom protocol implementation.","intents":["I want my agent to communicate with other OPVS agents using standard protocol","I need to discover capabilities of other agents in the OPVS network","I'm building a multi-agent system that requires standardized message routing"],"best_for":["Multi-agent systems built on OPVS Protocol","Teams standardizing agent communication across heterogeneous implementations","Builders creating agent networks with capability discovery"],"limitations":["Protocol compliance overhead adds latency compared to direct API calls","Capability discovery requires agents to register and advertise capabilities upfront","Message routing depends on OPVS network topology — may not scale to very large agent networks"],"requires":["OPVS Protocol specification compliance","Agent registry or service discovery mechanism","Network connectivity between agents"],"input_types":["OPVS protocol messages","agent identifiers and capability queries","request/response payloads"],"output_types":["OPVS protocol responses","capability advertisements","routing confirmations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-opvs-aimcp__cap_5","uri":"capability://safety.moderation.opvs.auth.credential.management","name":"opvs-auth-credential-management","description":"Exposes OPVS Auth system as MCP tools for credential management, token generation, and permission verification. 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Uses package metadata and client hints to recommend appropriate package variants based on client capabilities and constraints.","intents":["I'm an Antigravity user and need to use OPVS skills without hitting tool caps","I want to use only specific OPVS skills without loading unnecessary capabilities","I need to understand which OPVS package variant is right for my client"],"best_for":["Antigravity users requiring scoped skill packages","Teams optimizing MCP package selection based on client constraints","Builders managing multiple OPVS package variants"],"limitations":["Routing logic is documentation-based — no automatic client detection","Scoped packages require separate installation and configuration","No unified tool namespace across scoped packages — agents must know which package provides each tool"],"requires":["Knowledge of client tool cap constraints","Access to scoped @opvs-ai/mcp-<skill> packages","Package management tooling (npm, yarn, etc.)"],"input_types":["client type identifiers (Antigravity, Claude Code, Cursor)","skill requirements"],"output_types":["package recommendations","installation instructions","capability mappings"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ (typical MCP server requirement)","MCP client supporting Model Context Protocol (Claude Code, Cursor, or compatible)","Valid OPVS credentials/API keys for Auth skill initialization","Active AgentBoard workspace","Valid OPVS Auth credentials with AgentBoard access","MCP client with tool-calling support","Source code repository access (local or remote)","AgentDocs service or compatible documentation backend","Sufficient storage for documentation indices","AgentMemory service or compatible persistent store"],"failure_modes":["Monolithic approach increases MCP payload size compared to scoped packages — may impact latency on bandwidth-constrained connections","All 6 skills load regardless of which are actually used, adding memory overhead","Antigravity users should use scoped @opvs-ai/mcp-<skill> packages instead due to different tool cap constraints","MCP tool calls are stateless — complex board queries may require multiple round-trips","No built-in subscription/webhook support for real-time board updates — agents must poll for changes","Permission model depends on AgentBoard's auth system; MCP tools inherit caller's permissions","Indexing large codebases may exceed MCP message size limits — requires batching or streaming","Documentation quality depends on source code quality and comment coverage","No real-time sync — agents see stale documentation until re-indexing is triggered","Memory retrieval latency depends on storage backend — may add 50-200ms per query","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.23613984025920695,"quality":0.43,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:23.904Z","last_scraped_at":"2026-05-03T14:24:02.780Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":758,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=npm-opvs-aimcp","compare_url":"https://unfragile.ai/compare?artifact=npm-opvs-aimcp"}},"signature":"Ov0T+I5w7OeMXWLfaLJSv7/p9aK855khJDRufbA36wY2Y4vwDD3QbqxHIUuUZ5KL7iodQW3rZj3EBWaXbIDiDw==","signedAt":"2026-06-22T13:27:12.992Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-opvs-aimcp","artifact":"https://unfragile.ai/npm-opvs-aimcp","verify":"https://unfragile.ai/api/v1/verify?slug=npm-opvs-aimcp","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"}}