@opvs-ai/mcp
MCP ServerFreeOPVS MCP Server — all 6 public OPVS skills (AgentBoard, AgentDocs, AgentMemory, OPVS Protocol, Auth, Integrations) in one MCP. For clients without per-MCP tool caps (Claude Code, Cursor). Antigravity users should use the scoped @opvs-ai/mcp-<skill> packag
Capabilities9 decomposed
unified-mcp-skill-aggregation
Medium confidenceAggregates 6 discrete OPVS skills (AgentBoard, AgentDocs, AgentMemory, OPVS Protocol, Auth, Integrations) into a single MCP server endpoint, eliminating per-MCP tool caps by bundling all capabilities under one Model Context Protocol connection. Uses a skill registry pattern to expose heterogeneous tool sets through a unified transport layer, allowing Claude Code and Cursor to access the full OPVS ecosystem without hitting individual tool limits.
Bundles 6 heterogeneous OPVS skills into a single MCP server to bypass per-MCP tool caps in Claude Code and Cursor, whereas competitors typically expose skills as separate MCP instances or require manual tool registration
Eliminates tool cap friction for power users by consolidating all OPVS capabilities into one MCP connection, whereas managing 6 separate scoped packages requires multiple MCP registrations and context switching
agentboard-collaborative-workspace-access
Medium confidenceExposes AgentBoard's collaborative workspace and task management capabilities through MCP tool bindings, enabling AI agents to create, read, update, and delete board items, manage task hierarchies, and synchronize state across multiple agent instances. Implements a stateless tool interface that maps MCP calls to AgentBoard's REST API or internal data model, preserving board structure and permission semantics.
Exposes AgentBoard's task hierarchy and collaborative state as MCP tools, allowing agents to treat board operations as first-class capabilities rather than requiring custom API integration code
Provides native MCP bindings to AgentBoard's workspace model, whereas generic REST API clients require agents to handle HTTP orchestration and state management manually
agentdocs-codebase-documentation-indexing
Medium confidenceIntegrates AgentDocs' documentation indexing and retrieval system as MCP tools, enabling agents to index code repositories, generate documentation, and query structured documentation artifacts. Uses a document embedding and retrieval pattern to make codebase knowledge queryable by agents without requiring them to parse source files directly.
Exposes AgentDocs' documentation generation and semantic search as MCP tools, allowing agents to treat documentation as a queryable knowledge base rather than static files
Provides agent-native documentation indexing and retrieval, whereas RAG systems require agents to manage embeddings and vector stores separately
agentmemory-persistent-context-management
Medium confidenceExposes 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. Implements a key-value or document store interface where agents can write structured memory entries and query them with semantic or exact-match retrieval, preserving agent state without relying on prompt context alone.
Exposes AgentMemory as MCP tools for persistent agent state, allowing agents to maintain context across sessions without relying on prompt engineering or external state management
Provides native MCP bindings for agent memory, whereas generic databases require agents to implement their own serialization and retrieval logic
opvs-protocol-agent-communication
Medium confidenceImplements OPVS Protocol as MCP tools, enabling agents to communicate using OPVS' standardized message format and routing semantics. 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.
Exposes OPVS Protocol as MCP tools, standardizing agent-to-agent communication and capability discovery without requiring agents to implement protocol logic
Provides protocol-compliant agent communication through MCP, whereas custom agent frameworks require implementing routing and discovery from scratch
opvs-auth-credential-management
Medium confidenceExposes OPVS Auth system as MCP tools for credential management, token generation, and permission verification. Implements OAuth2/JWT patterns for secure agent authentication, allowing agents to obtain and refresh credentials, verify permissions, and access protected OPVS resources without embedding secrets in prompts or code.
Exposes OPVS Auth as MCP tools for credential management, allowing agents to authenticate and verify permissions without embedding secrets or implementing OAuth logic
Provides centralized credential management through MCP, whereas custom auth integration requires agents to handle token lifecycle and permission checks manually
opvs-integrations-third-party-service-binding
Medium confidenceExposes OPVS Integrations framework as MCP tools, enabling agents to connect to third-party services (APIs, databases, webhooks) through pre-configured integration adapters. Uses an adapter registry pattern where integrations are defined declaratively and exposed as MCP tools, allowing agents to invoke external services without implementing service-specific logic.
Exposes pre-configured third-party service integrations as MCP tools through an adapter registry, allowing agents to invoke external services without implementing service-specific logic
Provides declarative service integration through MCP adapters, whereas generic HTTP clients require agents to implement authentication, error handling, and response parsing for each service
mcp-tool-schema-exposure
Medium confidenceAutomatically exposes all 6 OPVS skills' capabilities as MCP-compliant tool schemas with JSON Schema definitions, parameter validation, and return type specifications. Implements the Model Context Protocol's tool definition format, allowing MCP clients (Claude Code, Cursor) to discover available tools, validate inputs, and handle outputs without requiring manual schema registration.
Automatically generates and exposes MCP-compliant tool schemas for all 6 OPVS skills, enabling seamless tool discovery and validation in MCP clients without manual schema registration
Provides automatic schema generation and exposure, whereas manual MCP integration requires hand-writing JSON Schema definitions for each tool
scoped-mcp-package-routing
Medium confidenceImplements conditional routing logic that directs Antigravity users to scoped @opvs-ai/mcp-<skill> packages instead of the monolithic MCP, optimizing for different tool cap constraints and deployment models. Uses package metadata and client hints to recommend appropriate package variants based on client capabilities and constraints.
Provides conditional routing guidance for monolithic vs scoped OPVS MCP packages based on client constraints, acknowledging that different clients have different tool cap limitations
Explicitly documents when to use scoped packages vs monolithic MCP, whereas competitors typically offer only one package variant
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with @opvs-ai/mcp, ranked by overlap. Discovered automatically through the match graph.
Buzz Killington
Provide prompts and documentation search capabilities to help LLM agents produce accurate and reliable code during development sessions. Enhance coding workflows by offering fact-checked answers, deep problem analysis, and trusted developer documentation search. Improve the quality and trustworthine
superpowers-zh
🦸 AI 编程超能力 · 中文增强版 — superpowers（116k+ ⭐）完整汉化 + 6 个中国原创 skills，让 Claude Code / Copilot CLI / Hermes Agent / Cursor / Windsurf / Kiro / Gemini CLI 等 16 款 AI 编程工具真正会干活
LLM Stack
No-code platform to build LLM Agents
Mastra/mcp-docs-server
** - Provides AI assistants with direct access to Mastra.ai's complete knowledge base.
OpenHands
🙌 OpenHands: AI-Driven Development
docfork
Docfork - Up-to-date Docs for AI Agents.
Best For
- ✓Claude Code and Cursor users who need full OPVS ecosystem access
- ✓AI agent builders requiring multi-skill orchestration in a single MCP context
- ✓Teams avoiding the complexity of managing 6 separate MCP server instances
- ✓AI agent developers building task-driven workflows with persistent state
- ✓Teams using AgentBoard as a central coordination hub for AI-assisted work
- ✓Builders creating multi-agent systems that need shared task visibility
- ✓AI code assistants that need codebase context without full source parsing
- ✓Documentation automation workflows driven by agents
Known Limitations
- ⚠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
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
Package Details
About
OPVS MCP Server — all 6 public OPVS skills (AgentBoard, AgentDocs, AgentMemory, OPVS Protocol, Auth, Integrations) in one MCP. For clients without per-MCP tool caps (Claude Code, Cursor). Antigravity users should use the scoped @opvs-ai/mcp-<skill> packag
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