# One MCP Server, Two AI Clients: Wiring Actian VectorAI DB Into Claude and Cursor > Source: > Published: 2026-08-29 13:54:56+00:00 **MCP promises one server, any AI client. I put that to the test by wiring Actian VectorAI DB up to Claude and Cursor with a single ~230-line Python file, and verified every claim about it at the protocol layer rather than taking anything on faith. Build's public on GitHub.** Every vector database has its own SDK. Every AI assistant has its own plugin format. Want Claude *and* Cursor to search your vector DB? That's normally two separate integrations, and a third tool next month makes three. MCP removes that last part. It's an open protocol (originally from Anthropic) that lets any MCP-aware client (Claude Desktop, Claude Code, Cursor, whatever comes next) talk to the *same* server over a standard interface. Write the server once, every client gets it for free. I built a small MCP server for **Actian VectorAI DB**, a portable, local-first vector database, to see if that promise holds up. Six tools, ~230 lines of Python, one file: | Tool | What it does | |---|---| `create_collection` | Idempotently creates a 384-dim collection with a chosen distance metric | `ingest_documents` | Embeds text and upserts it, payload included | `search` | Embeds a query, returns top-k ranked hits | `list_collections` | Lists what's in the instance | `get_collection_info` | Point count, status, vector config | `delete_collection` | Deletes it, no confirmation step | The one design decision I'd defend to the death: **the LLM never sees a vector.** Every tool's input and output is plain strings, lists, and JSON dicts. The embedding happens inside the server process (`sentence-transformers` running `all-MiniLM-L6-v2` locally on CPU), so Claude or Cursor only ever reasons about text and decides when to call `search` . LLMs are bad at generating 384 floating point numbers that mean something; they're good at calling a function with a `query: str` argument. ``` php Claude / Cursor <--stdio JSON-RPC--> server.py (FastMCP) <--gRPC--> Actian VectorAI DB │ sentence-transformers (local, 384-dim embeddings) ``` It's a reference implementation, sized for a stage: a single global DB, running locally, built around a demo-sized dataset (six sentences, for this build). Before locking in `get_collection_info` , I checked what the response looked like against the installed `actian-vectorai-client==1.0.2` , rather than coding purely off memory. Point count and status come back directly from that call, but not vector config (size/distance), so `get_collection_info` pulls those two things from two places: gRPC for point count and status, and the REST API (port 6573) for vector config. First, `examples/demo.py` running straight against the live DB, plain Python calling the SDK directly: created a collection, ingested 6 FAQ sentences, searched, and got the right result back. Then `fastmcp.Client` , to drive `server.py` over stdio JSON-RPC, the same path Claude and Cursor use. This is what confirmed `get_collection_info` was pulling the right data from both the gRPC and REST calls, something that only became visible once data flowed through the full protocol path. I also pointed the server at a dead port on purpose, just to confirm every tool failed with a plain-English message instead of a stack trace. At the OS process level, I confirmed Cursor itself had spawned the MCP server subprocess after reloading, proof the config was picked up and working. And finally, calling `search` through that MCP connection and getting back `{"text": "Submissions close Sunday at 9am."}` for "submission deadline for the hackathon," score `0.4227` . Create a collection called `notes` . `Collection 'notes' is ready (distance=cosine, dim=384).` Add these three facts to `notes` : submissions close Sunday at 9am, first prize is $2,000, teams can have 2-5 members. `Inserted 3 document(s) into collection 'notes'.` What's the submission deadline? `[{"score": 0.4777, "payload": {"text": "Submissions close Sunday at 9am."}}]` , which the assistant turns into "the deadline is Sunday at 9am." Then the point of the whole exercise: ask the same question in Cursor, switch windows, ask it again in Claude Desktop. Only the client-specific config block differs between the two. I picked Actian VectorAI DB for being local-first, no cloud dependency, which mattered for a live demo on conference wifi. The Community Edition is free and was enough for this entire build. If you want to spin it up yourself, the [Docker setup instructions](https://docs.vectoraidb.actian.com/home/installation/instructions) cover both a plain `docker run` and a `docker-compose.yml` . The bigger point, though: one small MCP server, any MCP client, natural language in, semantic search out, no vectors ever exposed to the model. If you've got an SDK and a thing worth calling from an LLM, this is basically the whole recipe. You can find the repo [here](https://github.com/gerimate/vectorai-mcp-server).