# Source: https://docs.crewai.com/en/tools/database-data/mongodbvectorsearchtool.md

> ## Documentation Index
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# MongoDB Vector Search Tool

> The `MongoDBVectorSearchTool` performs vector search on MongoDB Atlas with optional indexing helpers.

# `MongoDBVectorSearchTool`

## Description

Perform vector similarity queries on MongoDB Atlas collections. Supports index creation helpers and bulk insert of embedded texts.

MongoDB Atlas supports native vector search. Learn more:
[https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/)

## Installation

Install with the MongoDB extra:

```shell  theme={null}
pip install crewai-tools[mongodb]
```

or

```shell  theme={null}
uv add crewai-tools --extra mongodb
```

## Parameters

### Initialization

* `connection_string` (str, required)
* `database_name` (str, required)
* `collection_name` (str, required)
* `vector_index_name` (str, default `vector_index`)
* `text_key` (str, default `text`)
* `embedding_key` (str, default `embedding`)
* `dimensions` (int, default `1536`)

### Run Parameters

* `query` (str, required): Natural language query to embed and search.

## Quick start

```python Code theme={null}
from crewai_tools import MongoDBVectorSearchTool

tool = MongoDBVectorSearchTool(
  connection_string="mongodb+srv://...",
  database_name="mydb",
  collection_name="docs",
)

print(tool.run(query="how to create vector index"))
```

## Index creation helpers

Use `create_vector_search_index(...)` to provision an Atlas Vector Search index with the correct dimensions and similarity.

## Common issues

* Authentication failures: ensure your Atlas IP Access List allows your runner and the connection string includes credentials.
* Index not found: create the vector index first; name must match `vector_index_name`.
* Dimensions mismatch: align embedding model dimensions with `dimensions`.

## More examples

### Basic initialization

```python Code theme={null}
from crewai_tools import MongoDBVectorSearchTool

tool = MongoDBVectorSearchTool(
    database_name="example_database",
    collection_name="example_collection",
    connection_string="<your_mongodb_connection_string>",
)
```

### Custom query configuration

```python Code theme={null}
from crewai_tools import MongoDBVectorSearchConfig, MongoDBVectorSearchTool

query_config = MongoDBVectorSearchConfig(limit=10, oversampling_factor=2)
tool = MongoDBVectorSearchTool(
    database_name="example_database",
    collection_name="example_collection",
    connection_string="<your_mongodb_connection_string>",
    query_config=query_config,
    vector_index_name="my_vector_index",
)

rag_agent = Agent(
    name="rag_agent",
    role="You are a helpful assistant that can answer questions with the help of the MongoDBVectorSearchTool.",
    goal="...",
    backstory="...",
    tools=[tool],
)
```

### Preloading the database and creating the index

```python Code theme={null}
import os
from crewai_tools import MongoDBVectorSearchTool

tool = MongoDBVectorSearchTool(
    database_name="example_database",
    collection_name="example_collection",
    connection_string="<your_mongodb_connection_string>",
)

# Load text content from a local folder and add to MongoDB
texts = []
for fname in os.listdir("knowledge"):
    path = os.path.join("knowledge", fname)
    if os.path.isfile(path):
        with open(path, "r", encoding="utf-8") as f:
            texts.append(f.read())

tool.add_texts(texts)

# Create the Atlas Vector Search index (e.g., 3072 dims for text-embedding-3-large)
tool.create_vector_search_index(dimensions=3072)
```

## Example

```python Code theme={null}
from crewai import Agent, Task, Crew
from crewai_tools import MongoDBVectorSearchTool

tool = MongoDBVectorSearchTool(
    connection_string="mongodb+srv://...",
    database_name="mydb",
    collection_name="docs",
)

agent = Agent(
    role="RAG Agent",
    goal="Answer using MongoDB vector search",
    backstory="Knowledge retrieval specialist",
    tools=[tool],
    verbose=True,
)

task = Task(
    description="Find relevant content for 'indexing guidance'",
    expected_output="A concise answer citing the most relevant matches",
    agent=agent,
)

crew = Crew(
    agents=[agent], 
    tasks=[task],
    verbose=True,
)

result = crew.kickoff()
```