pgvector (Vector Database) MCP Server for Mastra AI 6 tools — connect in under 2 minutes
Mastra AI is a TypeScript-native agent framework built for modern web stacks. Connect pgvector (Vector Database) through the Vinkius and Mastra agents discover all tools automatically — type-safe, streaming-ready, and deployable anywhere Node.js runs.
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Vinkius supports streamable HTTP and SSE.
import { Agent } from "@mastra/core/agent";
import { createMCPClient } from "@mastra/mcp";
import { openai } from "@ai-sdk/openai";
async function main() {
// Your Vinkius token — get it at cloud.vinkius.com
const mcpClient = await createMCPClient({
servers: {
"pgvector-vector-database": {
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
},
});
const tools = await mcpClient.getTools();
const agent = new Agent({
name: "pgvector (Vector Database) Agent",
instructions:
"You help users interact with pgvector (Vector Database) " +
"using 6 tools.",
model: openai("gpt-4o"),
tools,
});
const result = await agent.generate(
"What can I do with pgvector (Vector Database)?"
);
console.log(result.text);
}
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.
Mastra's agent abstraction provides a clean separation between LLM logic and pgvector (Vector Database) tool infrastructure. Connect 6 tools through the Vinkius and use Mastra's built-in workflow engine to chain tool calls with conditional logic, retries, and parallel execution — deployable to any Node.js host in one command.
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 Mastra AI 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 Mastra AI via MCP
Follow these steps to integrate the pgvector (Vector Database) MCP Server with Mastra AI.
Install dependencies
Run npm install @mastra/core @mastra/mcp @ai-sdk/openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.ts and run with npx tsx agent.ts
Explore tools
Mastra discovers 6 tools from pgvector (Vector Database) via MCP
Why Use Mastra AI with the pgvector (Vector Database) MCP Server
Mastra AI provides unique advantages when paired with pgvector (Vector Database) through the Model Context Protocol.
Mastra's agent abstraction provides a clean separation between LLM logic and tool infrastructure — add pgvector (Vector Database) without touching business code
Built-in workflow engine chains MCP tool calls with conditional logic, retries, and parallel execution for complex automation
TypeScript-native: full type inference for every pgvector (Vector Database) tool response with IDE autocomplete and compile-time checks
One-command deployment to any Node.js host — Vercel, Railway, Fly.io, or your own infrastructure
pgvector (Vector Database) + Mastra AI Use Cases
Practical scenarios where Mastra AI combined with the pgvector (Vector Database) MCP Server delivers measurable value.
Automated workflows: build multi-step agents that query pgvector (Vector Database), process results, and trigger downstream actions in a typed pipeline
SaaS integrations: embed pgvector (Vector Database) as a first-class tool in your product's AI features with Mastra's clean agent API
Background jobs: schedule Mastra agents to query pgvector (Vector Database) on a cron and store results in your database automatically
Multi-agent systems: create specialist agents that collaborate using pgvector (Vector Database) tools alongside other MCP servers
pgvector (Vector Database) MCP Tools for Mastra AI (6)
These 6 tools become available when you connect pgvector (Vector Database) to Mastra AI 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 Mastra AI
Ready-to-use prompts you can give your Mastra AI 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 Mastra AI
Common issues when connecting pgvector (Vector Database) to Mastra AI through the Vinkius, and how to resolve them.
createMCPClient not exported
npm install @mastra/mcppgvector (Vector Database) + Mastra AI FAQ
Common questions about integrating pgvector (Vector Database) MCP Server with Mastra AI.
How does Mastra AI connect to MCP servers?
MCPClient with the server URL and pass it to your agent. Mastra discovers all tools and makes them available with full TypeScript types.Can Mastra agents use tools from multiple servers?
Does Mastra support workflow orchestration?
Connect pgvector (Vector Database) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Purpose-built IDE for agentic AI coding workflows.
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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 Mastra AI
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.