pgvector (Vector Database) MCP Server for Vercel AI SDK 6 tools — connect in under 2 minutes
The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect pgvector (Vector Database) through Vinkius and every tool is available as a typed function. ready for React Server Components, API routes, or any Node.js backend.
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Vinkius supports streamable HTTP and SSE.
import { createMCPClient } from "@ai-sdk/mcp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
async function main() {
const mcpClient = await createMCPClient({
transport: {
type: "http",
// Your Vinkius token. get it at cloud.vinkius.com
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
});
try {
const tools = await mcpClient.tools();
const { text } = await generateText({
model: openai("gpt-4o"),
tools,
prompt: "Using pgvector (Vector Database), list all available capabilities.",
});
console.log(text);
} finally {
await mcpClient.close();
}
}
main();
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About pgvector (Vector Database) MCP Server
Connect your PostgreSQL + pgvector database to any AI agent and manage vector embeddings, similarity searches, and index optimizations through natural conversation.
The Vercel AI SDK gives every pgvector (Vector Database) tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 6 tools through Vinkius and stream results progressively to React, Svelte, or Vue components. works on Edge Functions, Cloudflare Workers, and any Node.js runtime.
What you can do
- Vector Similarity Search — Run nearest-neighbor queries using cosine, L2, or inner product distance metrics across millions of embeddings with a single prompt.
- Table Management — Discover which tables contain vector columns, create new embedding tables with custom dimensions, and inspect your schema.
- Embedding CRUD — Insert, update, and delete individual vector entries with metadata, keeping your knowledge base fresh and accurate.
- Index Optimization — Create HNSW or IVFFlat indexes on vector columns to accelerate approximate nearest-neighbor (ANN) queries by orders of magnitude.
The pgvector (Vector Database) MCP Server exposes 6 tools through the Vinkius. Connect it to Vercel AI SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect pgvector (Vector Database) to Vercel AI SDK via MCP
Follow these steps to integrate the pgvector (Vector Database) MCP Server with Vercel AI SDK.
Install dependencies
Run npm install @ai-sdk/mcp ai @ai-sdk/openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the script
Save to agent.ts and run with npx tsx agent.ts
Explore tools
The SDK discovers 6 tools from pgvector (Vector Database) and passes them to the LLM
Why Use Vercel AI SDK with the pgvector (Vector Database) MCP Server
Vercel AI SDK provides unique advantages when paired with pgvector (Vector Database) through the Model Context Protocol.
TypeScript-first: every MCP tool gets full type inference, IDE autocomplete, and compile-time error checking out of the box
Framework-agnostic core works with Next.js, Nuxt, SvelteKit, or any Node.js runtime. same pgvector (Vector Database) integration everywhere
Built-in streaming UI primitives let you display pgvector (Vector Database) tool results progressively in React, Svelte, or Vue components
Edge-compatible: the AI SDK runs on Vercel Edge Functions, Cloudflare Workers, and other edge runtimes for minimal latency
pgvector (Vector Database) + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the pgvector (Vector Database) MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query pgvector (Vector Database) in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate pgvector (Vector Database) tools and return structured JSON responses to any frontend
Chatbots with tool use: embed pgvector (Vector Database) capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with pgvector (Vector Database) through natural language queries
pgvector (Vector Database) MCP Tools for Vercel AI SDK (6)
These 6 tools become available when you connect pgvector (Vector Database) to Vercel AI SDK via MCP:
create_index
Create vector index
create_table
Create vector table
delete_vector
Delete a vector
insert_vector
Insert a vector
list_tables
List tables
search_vectors
Vector similarity search
Example Prompts for pgvector (Vector Database) in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with pgvector (Vector Database) immediately.
"Show me all tables with vector columns in my database."
"Search for the 5 most similar documents to this query in the document_chunks table."
"Create a new table called 'support_tickets' with 1536-dimension vectors and an HNSW index."
Troubleshooting pgvector (Vector Database) MCP Server with Vercel AI SDK
Common issues when connecting pgvector (Vector Database) to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcppgvector (Vector Database) + Vercel AI SDK FAQ
Common questions about integrating pgvector (Vector Database) MCP Server with Vercel AI SDK.
How does the Vercel AI SDK connect to MCP servers?
createMCPClient from @ai-sdk/mcp and pass the server URL. The SDK discovers all tools and provides typed TypeScript interfaces for each one.Can I use MCP tools in Edge Functions?
Does it support streaming tool results?
useChat and streamText that handle tool calls and display results progressively in the UI.Connect pgvector (Vector Database) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect pgvector (Vector Database) to Vercel AI SDK
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.