# Convex MCP for AI Agents MCP

> Convex MCP gives your AI agent direct access to execute queries, mutations, and actions on your real-time Convex database backend. Instead of building complex integration layers, you simply ask your agent for the data or changes you need, letting it handle all the read/write logic against your live application state.

## Overview
- **Category:** loved-by-devs
- **Price:** Free
- **Tags:** real-time-database, serverless-functions, typescript, backend-as-a-service, acid-transactions

## Description

You can connect your AI client straight to your Convex backend using this MCP. It means your agent doesn't just talk about your app; it actually interacts with its database and serverless functions in real time. Think of it as giving your assistant the keys to your entire data layer.

Whether you need to pull a list of user records, update a project status, or trigger heavy background computation via an action, your agent handles it through natural conversation. You write 'get all active users who signed up last month,' and the MCP executes that query against Convex's tables instantly. If you need to modify data—like changing a subscription tier—it runs the mutation with full ACID guarantees right from the chat window.

It bypasses traditional API boilerplate entirely. Everything is managed through your AI client, which communicates with this MCP hosted on Vinkius. You get immediate visibility into your application's state and control over its logic without ever leaving your preferred interface.

## Tools

### run_action
This tool executes defined Convex action functions to run external logic and API calls.

### run_mutation
The agent uses this to modify data records, ensuring all changes are handled atomically with ACID guarantees.

### run_query
This tool retrieves documents and state by calling read-only Convex query functions.

### run_function
The agent calls a specific Convex function using its full URL identifier for maximum compatibility.

## Prompt Examples

**Prompt:** 
```
What are the top five users who haven't logged in this month?
```

**Response:** 
```
**Top Inactive Users**

| User ID | Name | Last Login |
| :---: | :---: | :---: |
| jd7a... | Marcus R. | 2024-10-01 |
| a8f3... | Chloe W. | 2024-09-28 |
| b1c2... | David P. | 2024-09-15 |

*These users are flagged for an outreach email campaign.*
```

**Prompt:** 
```
I need to create a new project record and assign it to the team lead, Alex.
```

**Response:** 
```
Done. The new project, **Aurora Initiative**, has been created successfully.

*   **Project ID:** p-983c
*   **Status:** Draft
*   **Owner:** Alex Smith (ID: a1b2...)

Please let me know if you need to run any associated actions, like notifying the team.
```

**Prompt:** 
```
Check if there are any active tasks that require review by the billing department.
```

**Response:** 
```
*Searching Convex records for pending tasks...*

Found 3 items requiring attention:

1. **Task ID: t-45k**: Billing Review (Client: BetaCorp)
2. **Task ID: t-67l**: License Renewal (Client: AlphaDev)
3. **Task ID: t-90m**: Usage Audit (Client: Gamma Inc.)

You can now ask me to update the status of any of these tasks.
```

## Capabilities

### Execute read-only queries against Convex tables
Your agent runs the run_query tool to fetch current data and application state.

### Commit transactional updates using run_mutation
The agent uses run_mutation to modify multiple records, ensuring all changes are consistent across your database.

### Trigger external logic via run_action
You can make the agent execute background actions for API calls or heavy computation using run_action.

### Call any function by URL identifier with run_function
The agent runs the run_function tool to call a specific Convex function regardless of its internal naming structure.

## Use Cases

### Validating a new feature's user impact
A Product Manager needs to know how many users in the last week viewed the checkout page but never completed a purchase. They ask their agent, which uses run_query, and instantly gets a count and list of IDs they can work with.

### Handling an urgent account update
A support team member discovers a user's premium status is incorrect. Instead of opening multiple admin panels, they tell their agent to run_mutation the record, and it updates the subscription tier immediately.

### Running post-deployment checks
A developer needs to confirm that a new background data sync worked correctly. They prompt their agent to check specific tables using run_query, validating state without needing a local environment setup.

### Triggering external webhooks from chat
The system requires an external payment service to be notified when a user reaches a certain milestone. The developer instructs the agent to use run_action, which sends the necessary API call and completes the workflow.

## Benefits

- Instant data access: Use the run_query tool to pull live documents and state without writing a single line of query language.
- Atomic updates: The run_mutation tool guarantees that when you change user records, all related fields update together or none do at all.
- Logic execution: Run complex side effects via run_action. Your agent can trigger external API calls directly from the chat.
- Flexible calling: With run_function, your agent can call any exposed backend function using its URL identifier for maximum compatibility across your app.
- Context switching eliminated: You keep debugging data and running migrations right inside your IDE or chat window.

## How It Works

The bottom line is you get to interact with your full-stack data layer using plain language prompts instead of writing boilerplate API calls.

1. First, you subscribe to this MCP and provide your Convex Deployment URL (and access key).
2. Next, when you talk to your AI client, simply ask it for a data action—like listing all records or updating a profile.
3. The agent translates that request into the correct function call (query, mutation, or action) and executes it directly against your live Convex environment.

## Frequently Asked Questions

**How does the Convex MCP help manage my database without writing code?**
It allows your AI agent to talk directly to your live database using natural language. You simply tell it what data you need or what change needs making, and the MCP executes the necessary query or mutation behind the scenes.

**Can I use the Convex MCP to check if a user record exists?**
Yes. You can ask your agent to run a simple read-only query against any table. It confirms the existence of records and pulls out exactly what you need, saving you manual checks.

**Is updating data with Convex MCP safe from errors?**
Absolutely. When you use mutation functions, the MCP handles all the transactions to ensure atomicity—meaning every piece of related data updates correctly together or nothing changes at all.

**What if I need to run a complex calculation that involves external APIs?**
You can trigger these side effects using actions. The MCP lets your agent execute defined Convex actions, which handle the heavy lifting and connection to external services for you.

**How does connecting this MCP improve my development workflow?**
It eliminates context switching. You can debug data integrity, check state, or run migrations directly from your chat interface alongside writing code, making the entire process faster and more efficient.