**Setup note:** MongoDB does not yet offer a hosted MCP endpoint, so this template requires running the official `mongodb-mcp-server` locally (one `npx` command — see setup steps below) before connecting from Agent Studio. Once running, you can talk to MongoDB the way you talk to a teammate. The server exposes your collections, aggregation framework, indexes and Atlas cluster controls as MCP tools — this template wraps them with a sensible system prompt for analytics, debugging and schema work. Works against any MongoDB connection string (local, Atlas, self-hosted) or Atlas service-account credentials for cluster management.
Default model
Claude Sonnet 4.5
MCP servers
Bring your own server URL
Auth
No token required
A few things this template does well out of the box.
Three steps to go from template to a live chat.
Click "Use this template"
Agent Studio opens with the MCP server, model and system prompt pre-filled.
Add your MCP server URL
Point the agent to your own MCP endpoint (HTTP or SSE).
Start chatting
Ask a question, watch live tool calls and switch models at any time to compare answers.
A quick walkthrough for the credential this template needs.
Copy one into the studio to see the agent in action.
List all collections in the `production` database with their document counts.
Show me the schema for the `orders` collection — sample 100 docs and infer field types.
Find users who signed up in the last 7 days and have not logged in since. Group by signup source.
Write an aggregation pipeline that returns daily order revenue for the last 30 days, broken down by product category.
Which indexes on the `events` collection have not been used in the last 24 hours?
The default instructions the model starts with. Edit it any time inside Agent Studio.
You are a MongoDB database assistant connected to a MongoDB cluster via the official mongodb-mcp-server. Use the available tools to: - Inspect databases, collections, schemas and indexes - Run find queries and aggregation pipelines to answer analytical questions - Profile slow queries and recommend index improvements - Manage Atlas clusters when service-account credentials are provided (list clusters, check status, surface metrics) Best practices: - Always use a sensible `limit` (default 50) on `find` queries to avoid returning huge result sets - Prefer aggregation pipelines over multiple round-trips for analytics questions - When suggesting indexes, explain the cardinality and query pattern that motivates the choice - Never run destructive operations (`drop`, `deleteMany`, mass updates) without explicit confirmation - For schema inference, sample at least 100 documents and call out fields that vary in type or are sometimes missing When you write a query or pipeline, show it in a code block before running it — users learn from seeing the MQL.
Open Agent Studio with this template pre-loaded. Add your token, pick any model, and start chatting.
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