How to Use the Vercel MCP in LlamaIndex

Q: How do I search Vercel deployment details using Vercel and LlamaIndex?

You index the results from getverceldeploymentinfo. This lets your application answer complex questions about specific deployments, like their associated environment variables, without needing to re-run the query every time.

Q: Can Vercel and LlamaIndex list all available projects?

Yes. The listvercelprojects tool populates a searchable index. Your agent can then answer questions like 'List all my projects that are related to the finance department.'.

Q: What if I need to know which teams have access with Vercel and LlamaIndex?

Use listvercelteams. This data is indexed, allowing your RAG application to answer 'Which team owns the marketing domain?' based on available account information.

Q: Does this MCP Server support checking user profiles with Vercel and LlamaIndex?

The getverceluserprofile tool retrieves profile data. This data can be indexed, allowing your knowledge base to recall specific user details later on.

Build knowledge bases with LlamaIndex using Vercel’s operational data.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Vercel MCP to LlamaIndex

Create your Vinkius account to connect Vercel to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Vercel with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Indexing Vercel Projects for Knowledge Retrieval

The `get_vercel_project_details` tool fetches project specifics, and you can index this output. This means your RAG application knows not just that a project exists, but its exact configuration details. It's searchable knowledge. You combine live API data with documents. If the answer is 'What version was deployed last week?', LlamaIndex uses `list_vercel_deployments` to ground the response in fact.

Searching Deployment History via MCP Server

If you need historical context, run `get_vercel_deployment_info`. Indexing these deployment records allows users to query past sessions. Instead of just getting a list of IDs, the knowledge base answers questions like 'Why did deployment XYZ fail?' This turns raw data endpoints into semantic search capabilities for your application.

Tracking Vercel Environments and Variables

Use `list_vercel_project_env_vars` to pull all current environment variables. LlamaIndex indexes this list, creating a searchable knowledge graph of what's configured across your projects. This setup lets you query configurations—for example, 'Which project uses the Stripe API key?'—and get an answer backed by the MCP Server data.

Setup guide

Set up Vercel MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Vercel MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Vercel tools.",
)
response = await agent.run("List recent Vercel data")

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Vercel. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Vercel now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Vercel MCP in LlamaIndex

You index the results from `get_vercel_deployment_info`. This lets your application answer complex questions about specific deployments, like their associated environment variables, without needing to re-run the query every time.

Yes. The `list_vercel_projects` tool populates a searchable index. Your agent can then answer questions like 'List all my projects that are related to the finance department.'.

Use `list_vercel_teams`. This data is indexed, allowing your RAG application to answer 'Which team owns the marketing domain?' based on available account information.

The `get_vercel_user_profile` tool retrieves profile data. This data can be indexed, allowing your knowledge base to recall specific user details later on.

The server handles project metadata, including environment variables, deployment IDs, and account domains. It is designed for structured configuration data, not personal user credentials.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript