"A map or mapping service" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Hybrid sparse-dense neural search with cross-encoder re-ranking and semantic retrieval over streamable HTTP.
Data Oil is a multi-model database built for object storage. It uses a log-structured storage engine, snapshot isolation with optimistic commit, and a change feed from the write-ahead log. Its SQL dialect supports joins, window functions, CTEs, set operations, graph traversal and pattern matching. It has its own HNSW vector index and BM25 full-text index, and a write is searchable by keyword and vector as soon as it commits. Access is over HTTP, the PostgreSQL wire protocol (extended protocol). We have a founder's program available for projects which may be of interest https://dataoil.tech/founders.html if your interested lets chat ;)
Persistent memory and knowledge graphs for AI agents. Hybrid search, context checkpoints, and more.
A search engine that learns from your grades. Store text, records, images or embeddings.
Stock-symbol vectors: which listed symbols an event, theme or headline moves, ranked in one call.
An MCP server for search and retrieval over a local LanceDB database
Semantic nearest-neighbour search over your datasets, plus indices and projections.
Stateless NMI + cosine fusion with entropy-driven alpha calibration
Remote ChromaDB vector database MCP server with streamable HTTP transport
识律法律检索 MCP（lex-platform-v1）：法条检索与条级引用解析（law_db 全目录 + Qdrant law_chunks_v2 语义检索）、类案… — 想 7×24 常驻 → 萬星
Vector RAG store for Word/Excel/PDF/PowerPoint. Break-even pricing, $5 per 5,700 pages.
Hosted Upstash MCP server: Redis, QStash, Workflow, Vector, Search, Box and Blob, with OAuth.
Astra DB tools for agents: schema, find, vector search, guarded writes, and interactive views.
RAG-as-a-service MCP sunucusu — çok-kiracılı koleksiyon yönetimi, metin ingest (chunk+embed+upsert,…
Xata MCP server lets AI agents interact with your Xata projects, and Postgres database branches.
Persistent memory and knowledge graph for AI assistants — keyword + vector + graph search.
Same functionality, consuming only 1/20 of the context window tokens.
