pgvector (Vector Database) MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add pgvector (Vector Database) as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to pgvector (Vector Database). "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in pgvector (Vector Database)?"
)
print(response)
asyncio.run(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.
LlamaIndex agents combine pgvector (Vector Database) tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the pgvector (Vector Database) MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 6 tools from pgvector (Vector Database)
Why Use LlamaIndex with the pgvector (Vector Database) MCP Server
LlamaIndex provides unique advantages when paired with pgvector (Vector Database) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine pgvector (Vector Database) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain pgvector (Vector Database) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query pgvector (Vector Database), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what pgvector (Vector Database) tools were called, what data was returned, and how it influenced the final answer
pgvector (Vector Database) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the pgvector (Vector Database) MCP Server delivers measurable value.
Hybrid search: combine pgvector (Vector Database) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query pgvector (Vector Database) to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying pgvector (Vector Database) for fresh data
Analytical workflows: chain pgvector (Vector Database) queries with LlamaIndex's data connectors to build multi-source analytical reports
pgvector (Vector Database) MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect pgvector (Vector Database) to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting pgvector (Vector Database) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcppgvector (Vector Database) + LlamaIndex FAQ
Common questions about integrating pgvector (Vector Database) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.