Convex MCP Server for LlamaIndexGive LlamaIndex instant access to 4 tools to Run Action, Run Function, Run Mutation, and more

Name: Convex
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Convex as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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The Convex MCP Server for LlamaIndex is a standout in the Loved By Devs category — giving your AI agent 4 tools to work with, ready to go from day one.

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python

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 Convex. "
            "You have 4 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Convex?"
    )
    print(response)

asyncio.run(main())

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About Convex MCP Server

Connect your Convex deployment to any AI agent and manage your application's data and logic through natural conversation. This server allows you to interact with your real-time database and serverless functions without leaving your AI interface.

LlamaIndex agents combine Convex tool responses with indexed documents for comprehensive, grounded answers. Connect 4 tools through 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

Data Fetching — Execute read-only queries to retrieve documents and state from your Convex tables.
Transactional Updates — Run mutations to modify data with full ACID guarantees directly from the agent.
Side Effects & APIs — Trigger Convex actions for external API calls, heavy computation, or non-transactional logic.
Flexible Execution — Call functions using standard colon notation or URL-style identifiers for maximum compatibility.

The Convex MCP Server exposes 4 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 4 Convex tools available for LlamaIndex

When LlamaIndex connects to Convex through Vinkius, your AI agent gets direct access to every tool listed below — spanning real-time-database, serverless-functions, typescript, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

run

Run action on Convex

Call a Convex action function

run

Run function on Convex

g., "messages/list" instead of "messages:list"). Call a Convex function by its URL identifier

run

Run mutation on Convex

Call a Convex mutation function

run

Run query on Convex

Use this for fetching data. Call a Convex query function

Connect Convex to LlamaIndex via MCP

Follow these steps to wire Convex into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

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 4 tools from Convex

Why Use LlamaIndex with the Convex MCP Server

LlamaIndex provides unique advantages when paired with Convex through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Convex tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Convex tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Convex, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Convex tools were called, what data was returned, and how it influenced the final answer

Convex + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Convex MCP Server delivers measurable value.

Hybrid search: combine Convex real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Convex 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 Convex for fresh data

Analytical workflows: chain Convex queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Convex in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Convex immediately.

"Run the Convex query 'messages:list' with no arguments."

"Call the mutation 'users:create' with the argument { "name": "Alice" }."

"Execute the function 'tasks/get_all' using run_function."

Troubleshooting Convex MCP Server with LlamaIndex

Common issues when connecting Convex to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Convex + LlamaIndex FAQ

Common questions about integrating Convex MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Convex tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.

Does LlamaIndex support async MCP calls?

Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.

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