MCP servers and skills: search, filter by where they run, and compare.
Explains hybrid search in Qdrant. Use when someone asks 'how do I setup hybrid search?', 'how to combine keyword and semantic search?', 'sparse plus dense vectors?', 'missing keyword matches', 'how to
OfficialSkillApache-2.0★ 256 in collectionNo key
Federated, local-first search for an AI — one query across transcripts, files, knowledge graph, vector store, and the web, fused by trust-weighted RRF. Apache-2.0.
PythonApache-2.0★ 28No key
Diagnoses and improves Qdrant search relevance. Use when someone reports 'search results are bad', 'wrong results', 'low precision', 'low recall', 'irrelevant matches', 'missing expected results', or
OfficialSkillApache-2.0★ 256 in collectionNo key
Diagnoses and fixes slow Qdrant search. Use when someone reports 'search is slow', 'high latency', 'queries take too long', 'low QPS', 'throughput too low', 'filtered search is slow', or 'search was f
OfficialSkillApache-2.0★ 256 in collectionNo key
Guides Qdrant search strategy selection. Use when someone asks 'should I use hybrid search?', 'how to rerank?', 'results are not relevant', 'I don't get needed results from my dataset but they're ther
OfficialSkillApache-2.0★ 256 in collectionNo key
Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid sear
OfficialSkillApache-2.0★ 21k in collectionNo key
Diagnoses Qdrant search quality issues. Use when someone reports 'results are bad', 'wrong results', 'not relevant results', 'missing matches', 'recall is low', 'approximate search worse than exact',
OfficialSkillApache-2.0★ 256 in collectionNo key
Constructing prefetch queries for hybrid retrieval, including sparse/dense and multi-field setups, and choosing a sparse embedding model. Use when someone asks 'dense and sparse in one search?', 'how
OfficialSkillApache-2.0★ 256 in collectionNo key
Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a
OfficialSkillApache-2.0★ 21k in collectionNo key
Agent skill that teaches LLMs to operate Milvus vector database using pymilvus — connection, collections, vector CRUD, search, hybrid search, full-text search, indexing, RBAC, and common patterns like
SkillApache-2.0★ 45No key
Redis integration for Google Agent Development Kit (ADK) - Memory, Sessions, Search Tools, MCP
OfficialSkillApache-2.0★ 31No key
Self-hosted knowledge service for AI agents: an LLM-maintained wiki and a business graph, searched in milliseconds, far faster than RAG, with no LLM or database on the read path. HTTP and MCP.
SkillApache-2.0★ 4Few starsNo key
Agent skills for Qdrant vector search: scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage across Python, TypeScript, Rust, Go,
OfficialSkillApache-2.0★ 260No key
Experimental local-first memory CLI for MCP agents: read-only Gmail, calendars, iMessage, and files → cited local context
GoApache-2.0★ 9No key
Fusing scores from multiple searches into a single ranked result (RRF, DBSF, custom fusion). Use when someone asks 'RRF or DBSF?', 'how to combine sparse and dense', 'how to combine scores from multip
OfficialSkillApache-2.0★ 256 in collectionNo key
Navigation hub linking sub-skills for proactive Qdrant tuning: search speed, indexing performance, and memory usage optimization. Use when planning configuration or capacity changes to improve speed a
OfficialSkillApache-2.0★ 256 in collectionNo key
MCP Server for Snowflake including Cortex AI, object management, SQL orchestration, semantic view consumption, and more
OfficialPythonApache-2.0★ 300No key
Official AI Agent skills for building with Zvec vector database
SkillApache-2.0★ 21No key
Expanding the candidate pool via relevance feedback, as an alternative to reranking when a dense retriever is too weak. Use when someone asks about 'Qdrant's Relevance Feedback API', 'improving dense
OfficialSkillApache-2.0★ 256 in collectionNo key
Guides Qdrant query throughput (QPS) scaling. Use when someone asks 'how to increase QPS', 'need more throughput', 'queries per second too low', 'batch search', 'read replicas', or 'how to handle more
OfficialSkillApache-2.0★ 256 in collectionNo key
Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend. ALWAYS USE THIS SKILL when the user mentions Pinecone, wants to index documents for semantic s
OfficialSkillApache-2.0★ 40k in collectionNo key
PDBe MCP Servers integrate Protein Data Bank Europe resources with LLMs via Model Context Protocol. Provides seamless access to protein structure data through API tools and graph database schema assis
PythonApache-2.0★ 42No key
TiDB AI SDK: Unified Multi-Modal Data Platform for AI Apps & Agents
PythonApache-2.0★ 33No key
An official Qdrant Model Context Protocol (MCP) server implementation
OfficialPythonApache-2.0★ 1.6kNo key
Who stands behind it
