Model Context Protocol (MCP) Server Directory

Embodied AI is moving out of the lab. This workflow shows how to wire a VLA model (vision frames in, natural-language commands in, low-level action tokens out) into a warehouse pick-and-place robot with real-time budgets, safety interlocks, and full telemetry.

Architect a LangGraph-powered orchestrator that polls real-time wholesale energy markets (PJM, ERCOT), forecasts prices, and time-shifts or migrates AI workloads to the cheapest, greenest compute region - including SMR-powered data centers.

Move beyond retrieve-then-generate. Build a self-correcting agentic graph RAG pipeline in LangGraph that fuses vector search with a knowledge graph, decomposes complex queries into sub-goals, performs multi-hop reasoning, grades every retrieved chunk, and rewrites queries in a retry loop until the evidence is solid.

Engineer a real-time voice agent that streams speech-to-text, orchestrates an LLM with tool calling, and synthesizes speech so the conversational loop stays under 300ms perceived latency, with confidence thresholds and human escalation as safety nets.

Give agents durable memory. Combine mem0 as the universal memory layer with LangGraph orchestration and a vector store to persist user, session, and agent-level context across sessions, channels, and devices, with adaptive updates that avoid memory bloat.

Build a Weaviate Vector Database MCP server for Claude Desktop and Cursor: BM25+dense hybrid search, vectorization, typed cross-references for graph RAG, and an OAuth 2.0 secured streamable-HTTP transport.

Enforce the OWASP Top 10 for LLM Applications 2026 from an MCP server: LLM01 prompt-injection blocking, Presidio PII redaction, Gitleaks secrets detection, and system-prompt-leak defense for Claude Desktop and Cursor.

OWASP's Top 10 for LLM Applications 2026 (released Aug 2026) adds vector and embedding weaknesses, maps every risk to NIST AI RMF and MITRE ATLAS, and turns agentic AI security into a repeatable audit. Here is the full checklist.

Vanilla RAG retrieves once and hopes. Agentic RAG plans sub-queries, retrieves iteratively, and verifies evidence before answering. Here is the 2026 architecture, a vanilla-vs-agentic comparison, and the honest latency and token-cost tradeoffs.

Real-time AI voice agents run on ~300–800ms latency budgets across ASR, reasoning LLM, and streaming TTS. Here is the full stack, per-stage budgets, VAD turn-taking with barge-in, and enterprise deployment patterns.

Pinecone vs Weaviate vs Milvus vs pgvector benchmarked for 2026 agent workloads: hybrid search, HNSW, sub-100ms ANN latency, cost, and the economics that make RAG about 1/10th the cost of fine-tuning.

Agent memory is the strategic layer of 2026 AI systems: long-term stores, working memory, and context engineering. Here is the agentic memory stack, the universal memory pattern (mem0, ~54K stars), and its cost economics.