How to Use the Convex MCP in LangChain

Q: Can LangChain run Convex mutations securely?

Yes, mutations executed via runmutation run under your configured Vinkius credentials. You control exactly which database paths the agent can modify through your server permissions.

Q: Does LangChain track Convex query performance automatically?

Yes, when you use runquery within your LangChain setup, LangSmith logs the entire execution. You get detailed latency metrics for every database read.

Q: What is the best way to call specific backend actions?

Use runfunction to invoke actions by their path. This allows the model to execute complex code on your backend without exposing raw database tables.

Run mutations and queries directly inside your LangChain pipelines with this Convex MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Convex MCP to LangChain

Create your Vinkius account to connect Convex to LangChain 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 Convex with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chaining Convex operations in LangChain

You can now feed database query results directly into subsequent chain steps. Use `run_query` to pull real-time records from your backend, then pass that exact data to your model without manual glue code. The model analyzes the fetched data and decides whether to write back using `run_mutation`. This turns static data fetching into a dynamic loop where your code reacts to actual database state on the fly.

Tracing Convex MCP Server calls in LangSmith

Stop guessing what your agent is doing to your database. Every single call to `run_action` or `run_function` gets logged automatically with full input and output payloads in your trace history. You see the latency of your mutations and the exact arguments passed to your backend. If a database transaction fails, you spot the bad payload immediately in your debugging console.

Executing complex backend logic on demand

Sometimes simple database queries are not enough, and you need to run heavy side effects. Triggering a `run_action` lets your agent kick off external API calls or heavy computations hosted on Convex. By calling `run_function` with standard slash-path formatting, your agent targets specific endpoints cleanly. This keeps your agent's system prompt simple while delegating the heavy lifting to your existing backend code.

Setup guide

Set up Convex MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Convex tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "convex-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Convex transactions"
    })
    print(result["messages"][-1].content)

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 Convex. 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 Convex 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 Convex MCP in LangChain

Install the MCP adapter and set up the multi-server client with your Vinkius endpoint. From there, pull the tools and pass them directly to your agent initialization function.

Yes, mutations executed via `run_mutation` run under your configured Vinkius credentials. You control exactly which database paths the agent can modify through your server permissions.

Yes, when you use `run_query` within your LangChain setup, LangSmith logs the entire execution. You get detailed latency metrics for every database read.

Use `run_function` to invoke actions by their path. This allows the model to execute complex code on your backend without exposing raw database tables.

Your database records and JSON documents are processed entirely in memory within a zero-trust sandbox. Vinkius never writes this data to disk or uses it for model training.

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