---
title: "MongoDB Atlas Streams"
description: "Build, operate, and debug Atlas Stream Processing (ASP) pipelines using four MCP tools from the MongoDB MCP Server."
type: skill
canonical_url: https://claudary.paisolsolutions.com/skills/skill-192
source: "Claudary"
difficulty: intermediate
author: "Claude Code Knowledge Pack"
date: 2026-07-10T11:44:11.502Z
license: CC-BY-4.0
attribution: "MongoDB Atlas Streams — Claudary (https://claudary.paisolsolutions.com/skills/skill-192)"
---

# MongoDB Atlas Streams
Build, operate, and debug Atlas Stream Processing (ASP) pipelines using four MCP tools from the MongoDB MCP Server.

## Overview

---
name: atlas-stream-processing
description: "Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials."
metadata:
  version: 1.0.0
  user-invocable: "true"
license: Apache-2.0
---

# MongoDB Atlas Streams

Build, operate, and debug Atlas Stream Processing (ASP) pipelines using four MCP tools from the MongoDB MCP Server.

## Prerequisites

This skill requires the **MongoDB MCP Server** connected with:
- Atlas API credentials (`apiClientId` and `apiClientSecret`)

The 4 tools: `atlas-streams-discover`, `atlas-streams-build`, `atlas-streams-manage`, `atlas-streams-teardown`.

**All operations require an Atlas project ID.** If unknown, call `atlas-list-projects` first to find your project ID.

## If MCP tools are unavailable

If the MongoDB MCP Server is not connected or the streams tools are missing, see [references/mcp-troubleshooting.md](references/mcp-troubleshooting.md) for diagnostic steps and fallback options.

## Tool Selection Matrix

### atlas-streams-discover — ALL read operations
| Action | Use when |
|--------|----------|
| `list-workspaces` | See all workspaces in a project |
| `inspect-workspace` | Review workspace config, state, region |
| `list-connections` | See all connections in a workspace |
| `inspect-connection` | Check connection state, config, health |
| `list-processors` | See all processors in a workspace |
| `inspect-processor` | Check processor state, pipeline, config |
| `diagnose-processor` | Full health report: state, stats, errors |
| `get-networking` | PrivateLink and VPC peering details. Optional: `cloudProvider` + `region` to get Atlas account details for PrivateLink setup |

**Pagination** (all list actions): `limit` (1-100, default 20), `pageNum` (default 1).
**Response format**: `responseFormat` — `"concise"` (default for list actions) or `"detailed"` (default for inspect/diagnose).

### atlas-streams-build — ALL create operations
| Resource | Key parameters |
|----------|---------------|
| `workspace` | `cloudProvider`, `region`, `tier` (default SP10), `includeSampleData` |
| `connection` | `connectionName`, `connectionType` (Kafka/Cluster/S3/Https/Kinesis/Lambda/SchemaRegistry/Sample), `connectionConfig` |
| `processor` | `processorName`, `pipeline` (must start with `$source`, end with `$merge`/`$emit`), `dlq`, `autoStart` |
| `privatelink` | `privateLinkConfig` (project-level, not tied to a specific workspace) |

**Field mapping — only fill fields for the selected resource type:**

- **resource = "workspace":** Fill: `projectId`, `workspaceName`, `cloudProvider`, `region`, `tier`, `includeSampleData`. Leave empty: all connection and processor fields.
- **resource = "connection":** Fill: `projectId`, `workspaceName`, `connectionName`, `connectionType`, `connectionConfig`. Leave empty: all workspace and processor fields. (See [references/connection-configs.md](references/connection-configs.md) for type-specific schemas.)
- **resource = "processor":** Fill: `projectId`, `workspaceName`, `processorName`, `pipeline`, `dlq` (recommended), `autoStart` (optional). Leave empty: all workspace and connection fields. (See [references/pipeline-patterns.md](references/pipeline-patterns.md) for pipeline examples.)
- **resource = "privatelink":** Fill: `projectId`, `privateLinkConfig`. Note: PrivateLink is **project-level**, not workspace-level. `workspaceName` is not required — omit it. Leave empty: all connection and processor fields.

### atlas-streams-manage — ALL update/state operations
| Action | Notes |
|--------|-------|
| `start-processor` | Begins billing. Optional `tier` override, `resumeFromCheckpoint` |
| `stop-processor` | Stops billing. Retains state 45 days |
| `modify-processor` | Processor must be stopped first. Change pipeline, DLQ, or name |
| `update-workspace` | Change tier or region |
| `update-connection` | Update config (networking is immutable — must delete and recreate) |
| `accept-peering` / `reject-peering` | VPC peering management |

**Field mapping** — always fill `projectId`, `workspaceName`, then by action:

- `"start-processor"` → `resourceName`. Optional: `tier`, `resumeFromCheckpoint`, `startAtOperationTime` (ISO 8601 timestamp to resume from a specific point)
- `"stop-processor"` → `resourceName`
- `"modify-processor"` → `resourceName`. At least one of: `pipeline`, `dlq`, `newName`
- `"update-workspace"` → `newRegion` or `newTier`
- `"update-connection"` → `resourceName`, `connectionConfig`. **Exception: networking config (e.g., PrivateLink) cannot be modified after creation** — delete and recreate.
- `"accept-peering"` → `peeringId`, `requesterAccountId`, `requesterVpcId`
- `"reject-peering"` → `peeringId`

**State pre-checks:**
- `start-processor` → errors if processor is already STARTED
- `stop-processor` → no-ops if already STOPPED or CREATED (not an error)
- `modify-processor` → errors if processor is STARTED (must stop first)

**Processor states:** `CREATED` → `STARTED` (via start) → `STOPPED` (via stop). Can also enter `FAILED` on runtime errors. Modify requires STOPPED or CREATED state.

**Teardown safety checks:**
- **Processor deletion** → auto-stops before deleting (no need to stop manually first)
- **Connection deletion** → blocks if any running processor references it. Stop/delete referencing processors first.
- **Workspace deletion** → See detailed workflow below (lines 108-111).

### atlas-streams-teardown — ALL delete operations
| Resource | Safety behavior |
|----------|----------------|
| `processor` | Auto-stops before deleting |
| `connection` | Blocks if referenced by running processor |
| `workspace` | Cascading delete of all connections and processors |
| `privatelink` / `peering` | Remove networking resources |

**Field mapping** — always fill `projectId`, `resource`, then:

- `resource: "workspace"` → `workspaceName`
- `resource: "connection"` or `"processor"` → `workspaceName`, `resourceName`
- `resource: "privatelink"` or `"peering"` → `resourceName` (the ID). These are project-level resources, not tied to a specific workspace.

**Before deleting a workspace**, inspect it first:
1. `atlas-streams-discover` → `inspect-workspace` — get connection/processor counts
2. Present to user: "Workspace X contains N connections and M processors. Deleting permanently removes all. Proceed?"
3. Wait for confirmation before calling `atlas-streams-teardown`

## CRITICAL: Validate Before Creating Processors

**You MUST call `search-knowledge` before composing any processor pipeline.** This is not optional.
- **Field validation:** Query with the sink/source type, e.g. "Atlas Stream Processing $emit S3 fields" or "Atlas Stream Processing Kafka $source configuration". This catches errors like `prefix` vs `path` for S3 `$emit`.
- **Pattern examples:** Query with `dataSources: [{"name": "devcenter"}]` for working pipelines, e.g. "Atlas Stream Processing tumbling window example".

Also fetch examples from the official ASP examples repo when building non-trivial processors: **https://github.com/mongodb/ASP_example** (quickstarts, example processors, Terraform examples). Start with `example_processors/README.md` for the full pattern catalog.

Key quickstarts:
| Quickstart | Pattern |
|-----------|---------|
| `00_hello_world.json` | Inline `$source.documents` with `$match` (zero infra, ephemeral) |
| `01_changestream_basic.json` | Change stream → tumbling window → `$merge` to Atlas |
| `03_kafka_to_mongo.json` | Kafka source → tumbling window rollup → `$merge` to Atlas |
| `04_mongo_to_mongo.json` | Chained processors: rollup → archive to separate collection |
| `05_kafka_tail.json` | Real-time Kafka topic monitoring (sinkless, like `tail -f`) |

## Pipeline Rules & Warnings

**Invalid constructs** — these are NOT valid in streaming pipelines:
- **`$$NOW`**, **`$$ROOT`**, **`$$CURRENT`** — NOT available in stream processing. NEVER use these. Use the document's own timestamp field or `_stream_meta` metadata for event time instead of `$$NOW`.
- **HTTPS connections as `$source`** — HTTPS is for `$https` enrichment or sink only, NOT as a data source
- **Kafka `$source` without `topic`** — topic field is required
- **Pipelines without a sink** — terminal stage (`$merge`, `$emit`, `$https`, or `$externalFunction` async) required for deployed processors (sinkless only works via `sp.process()`)
- **Lambda as `$emit` target** — Lambda uses `$externalFunction` (mid-pipeline enrichment), not `$emit`
- **`$validate` with `validationAction: "error"`** — crashes processor; use `"dlq"` instead

**Required fields by stage:**
- **`$source` (change stream)**: include `fullDocument: "updateLookup"` to get the full document content
- **`$source` (Kinesis)**: use `stream` (NOT `streamName` or `topic`)
- **`$emit` (Kinesis)**: MUST include `partitionKey`
- **`$emit` (S3)**: use `path` (NOT `prefix`)
- **`$https`**: must include `connectionName`, `path`, `method`, `as`, `onError: "dlq"`
- **`$externalFunction`**: must include `connectionName`, `functionName`, `execution`, `as`, `onError: "dlq"`
- **`$validate`**: must include `validator` with `$jsonSchema` and `validationAction: "dlq"`
- **`$lookup`**: include `parallelism` setting (e.g., `parallelism: 2`) for concurrent I/O
- **AWS connections** (S3, Kinesis, Lambda): IAM role ARN must be registered via Atlas Cloud Provider Access first. Always confirm this with user. See [references/connection-configs.md](references/connection-configs.md) for details.

See [references/pipeline-patterns.md](references/pipeline-patterns.md) for stage field examples with JSON syntax.

**SchemaRegistry connection:** `connectionType` must be `"SchemaRegistry"` (not `"Kafka"`). Schema type values are case-sensitive (use lowercase `avro`, not `AVRO`). See [references/connection-configs.md](references/connection-configs.md#schemaregistry) for required fields and auth types.

## MCP Tool Behaviors

**Elicitation:** When creating connections, the build tool auto-collects missing sensitive fields (passwords, bootstrap servers) via MCP elicitation. Do NOT ask the user for these — let the tool collect them.

**Auto-normalization:**
- `bootstrapServers` array → auto-converted to comma-separated string
- `schemaRegistryUrls` string → auto-wrapped in array
- `dbRoleToExecute` → defaults to `{role: "readWriteAnyDatabase", type: "BUILT_IN"}` for Cluster connections

**Workspace creation:** `includeSampleData` defaults to `true`, which auto-creates the `sample_stream_solar` connection.

**Region naming:** The `region` field uses Atlas-specific names that differ by cloud provider. Using the wrong format returns a cryptic `dataProcessRegion` error.

| Provider | Cloud Region | Streams `region` Value |
|----------|-------------|----------------------|
| **AWS** | us-east-1 | `VIRGINIA_USA` |
| **AWS** | us-east-2 | `OHIO_USA` |
| **AWS** | eu-west-1 | `DUBLIN_IRL` |
| **GCP** | us-central1 | `US_CENTRAL1` |
| **GCP** | europe-west1 | `EUROPE_WEST1` |
| **Azure** | eastus | `eastus` |
| **Azure** | westeurope | `westeurope` |

See [references/connection-configs.md](references/connection-configs.md) for the full region mapping table. If unsure, inspect an existing workspace with `atlas-streams-discover` → `inspect-workspace` and check `dataProcessRegion.region`.

## Connection Capabilities — Source/Sink Reference

Know what each connection type can do before creating pipelines:

| Connection Type | As Source ($source) | As Sink ($merge / $emit) | Mid-Pipeline | Notes |
|-----------------|---------------------|--------------------------|--------------|-------|
| **Cluster** | ✅ Change streams | ✅ $merge to collections | ✅ $lookup | Change streams monitor insert/update/delete/replace operations |
| **Kafka** | ✅ Topic consumer | ✅ $emit to topics | ❌ | Source MUST include `topic` field |
| **Sample Stream** | ✅ Sample data | ❌ Not valid | ❌ | Testing/demo only |
| **S3** | ❌ Not valid | ✅ $emit to buckets | ❌ | Sink only - use `path`, `format`, `compression`. Supports AWS PrivateLink. |
| **Https** | ❌ Not valid | ✅ $https as sink | ✅ $https enrichment | Can be used mid-pipeline for enrichment OR as final sink stage |
| **AWSLambda** | ❌ Not valid | ✅ $externalFunction (async only) | ✅ $externalFunction (sync or async) | **Sink:** `execution: "async"` required. **Mid-pipeline:** `execution: "sync"` or `"async"` |
| **AWS Kinesis** | ✅ Stream consumer | ✅ $emit to streams | ❌ | Similar to Kafka pattern |
| **SchemaRegistry** | ❌ Not valid | ❌ Not valid | ✅ Schema resolution | **Metadata only** - used by Kafka connections for Avro schemas |

**Common connection usage mistakes to avoid:**
- ❌ Using `$externalFunction` as sink with `execution: "sync"` → Must use `execution: "async"` for sink stage
- ❌ Forgetting change streams exist → Atlas Cluster is a powerful source, not just a sink
- ❌ Using `$merge` with Kafka → Use `$emit` for Kafka sinks

See [references/connection-configs.md](references/connection-configs.md) for detailed connection configuration schemas by type.

## Core Workflows

### Setup from scratch
1. `atlas-streams-discover` → `list-workspaces` (check existing)
2. `atlas-streams-build` → `resource: "workspace"` (region near data, SP10 for dev)
3. `atlas-streams-build` → `resource: "connection"` (for each source/sink/enrichment)
4. **Validate connections:** `atlas-streams-discover` → `list-connections` + `inspect-connection` for each — verify names match targets, present summary to user
5. Call `search-knowledge` to validate field names. Fetch relevant examples from https://github.com/mongodb/ASP_example
6. `atlas-streams-build` → `resource: "processor"` (with DLQ configured)
7. `atlas-streams-manage` → `start-processor` (warn about billing)

### Workflow Patterns

**Incremental pipeline development (recommended):**
See [references/development-workflow.md](references/development-workflow.md) for the full 5-phase lifecycle.
1. Start with basic `$source` → `$merge` pipeline (validate connectivity)
2. Add `$match` stages (validate filtering)
3. Add `$addFields` / `$project` transforms (validate reshaping)
4. Add windowing or enrichment (validate aggregation logic)
5. Add error handling / DLQ configuration

**Modify a processor pipeline:**
1. `atlas-streams-manage` → `action: "stop-processor"` — **processor MUST be stopped first**
2. `atlas-streams-manage` → `action: "modify-processor"` — provide new pipeline
3. `atlas-streams-manage` → `action: "start-processor"` — restart

**Debug a failing processor:**
1. `atlas-streams-discover` → `diagnose-processor` — one-shot health report. Always call this first.
2. **Commit to a specific root cause.** Match symptoms to diagnostic patterns:
   - **Error

---

Source: [Claudary](https://claudary.paisolsolutions.com/skills/skill-192) · https://claudary.paisolsolutions.com