Skip to content

chore: clarify that search support is in preview #694

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 3 commits into from
Oct 29, 2025
Merged

chore: clarify that search support is in preview #694

Changes from 1 commit
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
161fe37
chore: clarify that search support is in preview
nirinchev Oct 27, 2025
f3d4481
reformat readme
nirinchev Oct 28, 2025
ec5c1df
remove private again
nirinchev Oct 29, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 3 additions & 3 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -368,7 +368,7 @@ The MongoDB MCP Server can be configured using multiple methods, with the follow
| `exportTimeoutMs` | `MDB_MCP_EXPORT_TIMEOUT_MS` | 300000 | Time in milliseconds after which an export is considered expired and eligible for cleanup. |
| `exportCleanupIntervalMs` | `MDB_MCP_EXPORT_CLEANUP_INTERVAL_MS` | 120000 | Time in milliseconds between export cleanup cycles that remove expired export files. |
| `atlasTemporaryDatabaseUserLifetimeMs` | `MDB_MCP_ATLAS_TEMPORARY_DATABASE_USER_LIFETIME_MS` | 14400000 | Time in milliseconds that temporary database users created when connecting to MongoDB Atlas clusters will remain active before being automatically deleted. |
| `voyageApiKey` | `MDB_VOYAGE_API_KEY` | <not set> | API key for communicating with Voyage AI. Used for generating embeddings for Vector search. |
| ([preview](#opting-into-preview-features)) `voyageApiKey` | `MDB_VOYAGE_API_KEY` | <not set> | API key for communicating with Voyage AI. Used for generating embeddings for Vector search. **This feature is in preview and requires opting into the `vectorSearch` preview feature**. |
Copy link

Copilot AI Oct 27, 2025

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

The table formatting is broken due to inconsistent cell alignment. The preview notation and field name should be in the first column only, without extending into subsequent columns. The redundant note about preview at the end is also unnecessary since it's already indicated by the prefix.

Suggested change
| ([preview](#opting-into-preview-features)) `voyageApiKey` | `MDB_VOYAGE_API_KEY` | <not set> | API key for communicating with Voyage AI. Used for generating embeddings for Vector search. **This feature is in preview and requires opting into the `vectorSearch` preview feature**. |
| [preview](#opting-into-preview-features) `voyageApiKey` | `MDB_VOYAGE_API_KEY` | <not set> | API key for communicating with Voyage AI. Used for generating embeddings for Vector search. |

Copilot uses AI. Check for mistakes.
Copy link
Collaborator

@himanshusinghs himanshusinghs Oct 27, 2025

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Might wanna check this.

| `previewFeatures` | `MDB_MCP_PREVIEW_FEATURES` | `[]` | An array of preview features to opt into. |

#### Logger Options
Expand Down Expand Up @@ -501,8 +501,8 @@ The MongoDB MCP Server may offer functionality that is still in development and
List of available preview features:

- `vectorSearch` - Enables tools or functionality related to Vector Search in MongoDB Atlas:
- Index management, such as creating, listing, and dropping vector search indexes.
- Querying collections using vector search capabilities. This requires a configured embedding model that will be used to generate vector representations of the query data.
- Index management, such as creating, listing, and dropping search and vector search indexes.
- Querying collections using vector search capabilities. This requires a configured embedding model that will be used to generate vector representations of the query data. Currently, only [Voyage AI](https://www.voyageai.com) embedding models are supported. Set the `voyageApiKey` configuration option with your Voyage AI API key to use this feature.

### Atlas API Access

Expand Down
Loading