MongoDB Connector by Apoth3osis

Name: MongoDB Connector
Brand: Apoth3osis
SKU: 69c56fd1fd4227d57ee069c6
Price: 0.05 USD
Availability: InStock

MongoDB Connector

Connector

Available ActionsEach successful request consumes credits as outlined below.

find_documents^5crcount_documents^5crestimated_count^5crlist_databases^5crlist_collections^5craggregate^5crdistinct^5crlist_indexes^5crlist_search_indexes^5crinsert_documents^5crupdate_documents^5crreplace_document^5crfind_one_and_update^5crfind_one_and_delete^5crbulk_write^5crdelete_documents^5crcreate_index^5crdrop_index^5crcreate_search_index^5crupdate_search_index^5crdrop_search_index^5crcreate_collection^5crdrop_collection^5crrun_command^5cr

Details

Connect your own MongoDB database and query, insert, update, delete, and aggregate documents without leaving your workflow. Browse databases and collections, inspect indexes, and run full aggregation pipelines including joins, grouping, and windowed analytics. Create and manage Atlas Search indexes for full-text search and Atlas Vector Search indexes for semantic similarity and RAG applications. Export query results as downloadable CSV or JSON files for use in spreadsheets, dashboards, or data pipelines. Execute atomic find-and-modify operations and batch mixed writes in a single round trip with bulk_write. Manage collection lifecycles with create, drop, and schema validation. Access is fully permission-gated with four levels — read, write, delete, and admin — so you stay in control of what agents can do with your data. Works with MongoDB Atlas and any cloud-hosted or publicly accessible MongoDB instance.

Use Cases

Query MongoDB collections, Export data as CSV or JSON, Run aggregation pipelines, Atlas Vector Search for RAG, Full-text search with Atlas Search, Insert and update documents, Bulk write operations, Manage indexes, Create vector search indexes, Geospatial queries, Schema validation, Database administration

Dynamic MCP Setup

Connect once through AgentPMT Dynamic MCP, then use approved tools from the same agent connection.

30 Second Setup

STDIO connector for Claude Code, Codex, Cursor, Zed, and other LLMs that require STDIO or custom connections.

npm install -g @agentpmt/mcp-routeragentpmt-setup

Hosted Streamable HTTPS

MCP endpoint for browser-based apps like ChatGPT, Claude, Grok, or any time you want a streamable connection with no local install.

https://api.agentpmt.com/mcp

Config Example

Use the hosted endpoint directly in clients that support remote MCP. Store your Bearer token in the client config or secret field.

Full connection guide

{
  "mcpServers": {
    "agentpmt": {
      "type": "streamable-http",
      "url": "https://api.agentpmt.com/mcp",
      "headers": {
        "Authorization": "Bearer <AGENTPMT_BEARER_TOKEN>",
        "x-instance-metadata": "{\"client\":\"generic-mcp\",\"platform\":\"remote\"}"
      }
    }
  }
}

Need client videos, organization controls, audit details, and the full feature overview?

More About Dynamic MCP

Actions(24)

find_documents^5cr9 params(2 required)

Query documents with filter, projection, sort, and pagination. Supports CSV/JSON export.

databaserequiredstring

Database name

collectionrequiredstring

Collection name

filterobject

MongoDB query filter

projectionobject

Fields to include/exclude

sortarray

Sort as [[field, direction]] (1=asc, -1=desc)

Array of: array

limitinteger

Max documents (1-1000, default 20)

skipinteger

Documents to skip

outputarray

Output modes: inline, export, or both

Array of: string

export_formatstring

Export file format

Values:

jsoncsv

count_documents^5cr3 params(2 required)

Count documents matching a filter

databaserequiredstring

Database name

collectionrequiredstring

Collection name

filterobject

MongoDB query filter

estimated_count^5cr2 params(2 required)

Fast approximate document count using collection metadata

databaserequiredstring

Database name

collectionrequiredstring

Collection name

list_databases^5cr0 params

List all accessible databases

No parameters for this action.

list_collections^5cr1 param(1 required)

List collections in a database

databaserequiredstring

Database name

aggregate^5cr5 params(3 required)

Run an aggregation pipeline. Supports $search, $vectorSearch, $geoNear, $lookup, CSV/JSON export.

databaserequiredstring

Database name

collectionrequiredstring

Collection name

pipelinerequiredarray

Aggregation pipeline stages

Array of: object

outputarray

Output modes

Array of: string

export_formatstring

Export file format

Values:

jsoncsv

distinct^5cr4 params(3 required)

Get distinct values for a field

databaserequiredstring

Database name

collectionrequiredstring

Collection name

field_namerequiredstring

Field name

filterobject

MongoDB query filter

list_indexes^5cr2 params(2 required)

List all indexes on a collection

databaserequiredstring

Database name

collectionrequiredstring

Collection name

list_search_indexes^5cr2 params(2 required)

List Atlas Search and Vector Search indexes

databaserequiredstring

Database name

collectionrequiredstring

Collection name

insert_documents^5cr4 params(2 required)

Insert one or more documents

databaserequiredstring

Database name

collectionrequiredstring

Collection name

documentobject

Single document to insert

documentsarray

Multiple documents to insert

Array of: object

update_documents^5cr6 params(3 required)

Update one or many documents

databaserequiredstring

Database name

collectionrequiredstring

Collection name

updaterequiredobject

Update operations ($set, $inc, etc.)

filterobject

Match filter

upsertboolean

Create if no match

manyboolean

Update all matches (default: false)

replace_document^5cr5 params(3 required)

Replace a single document

databaserequiredstring

Database name

collectionrequiredstring

Collection name

documentrequiredobject

Replacement document

filterobject

Match filter

upsertboolean

Create if no match

find_one_and_update^5cr8 params(3 required)

Atomically find and update a document, returning it

databaserequiredstring

Database name

collectionrequiredstring

Collection name

updaterequiredobject

Update operations

filterobject

Match filter

projectionobject

Fields to return

sortarray

Sort to determine which doc

Array of: array

upsertboolean

Create if no match

return_documentstring

Return doc before or after update

Values:

beforeafter

find_one_and_delete^5cr5 params(2 required)

Atomically find and delete a document, returning it

databaserequiredstring

Database name

collectionrequiredstring

Collection name

filterobject

Match filter

projectionobject

Fields to return

sortarray

Sort to determine which doc

Array of: array

bulk_write^5cr3 params(3 required)

Execute mixed batch of insert, update, replace, and delete operations

databaserequiredstring

Database name

collectionrequiredstring

Collection name

operationsrequiredarray

Operations: [{operation: insert_one|update_one|update_many|replace_one|delete_one|delete_many, ...params}]

Array of: object

delete_documents^5cr4 params(2 required)

Delete one or many documents

databaserequiredstring

Database name

collectionrequiredstring

Collection name

filterobject

Match filter

manyboolean

Delete all matches (default: false)

create_index^5cr5 params(3 required)

Create an index (single, compound, text, geospatial, hashed, wildcard)

databaserequiredstring

Database name

collectionrequiredstring

Collection name

index_keysrequiredarray

Index keys [[field, 1/-1]]

Array of: array

index_namestring

Index name

index_optionsobject

Options (unique, sparse, TTL, partial, etc.)

drop_index^5cr3 params(3 required)

Drop an index from a collection

databaserequiredstring

Database name

collectionrequiredstring

Collection name

index_namerequiredstring

Index name to drop

create_search_index^5cr5 params(3 required)

Create an Atlas Search or Vector Search index

databaserequiredstring

Database name

collectionrequiredstring

Collection name

search_index_definitionrequiredobject

Search index definition (mappings or vector fields)

index_namestring

Index name

search_index_typestring

Index type (default: search)

Values:

searchvectorSearch

update_search_index^5cr4 params(4 required)

Update an Atlas Search or Vector Search index definition

databaserequiredstring

Database name

collectionrequiredstring

Collection name

index_namerequiredstring

Index name to update

search_index_definitionrequiredobject

New index definition

drop_search_index^5cr3 params(3 required)

Drop an Atlas Search or Vector Search index

databaserequiredstring

Database name

collectionrequiredstring

Collection name

index_namerequiredstring

Index name to drop

create_collection^5cr3 params(2 required)

Create a collection with optional schema validation, capping, or time series config

databaserequiredstring

Database name

collectionrequiredstring

Collection name

collection_optionsobject

Options: validator, capped, timeseries, collation, etc.

drop_collection^5cr2 params(2 required)

Drop a collection

databaserequiredstring

Database name

collectionrequiredstring

Collection name

run_command^5cr2 params(2 required)

Run any MongoDB database command (dbStats, collStats, serverStatus, etc.)

databaserequiredstring

Database name

commandrequiredobject

MongoDB command document

curl -X POST "https://api.agentpmt.com/products/purchase" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ********" \
  -d '{
    "product_id": "69c56fd1fd4227d57ee069c6",
    "parameters": {
      "action": "find_documents",
      "database": "example_database",
      "collection": "example_collection"
    }
  }'

import requests
import json

url = "https://api.agentpmt.com/products/purchase"

headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer ********"
}

data = {
    "product_id": "69c56fd1fd4227d57ee069c6",
    "parameters": {
        "action": "find_documents",
        "database": "example_database",
        "collection": "example_collection"
    }
}

response = requests.post(url, headers=headers, json=data)
print(response.status_code)
print(response.json())

const url = "https://api.agentpmt.com/products/purchase";

const headers = {
  "Content-Type": "application/json",
  "Authorization": "Bearer ********"
};

const data = {
  product_id: "69c56fd1fd4227d57ee069c6",
  parameters: {
    "action": "find_documents",
    "database": "example_database",
    "collection": "example_collection"
  }
};

fetch(url, {
  method: "POST",
  headers,
  body: JSON.stringify(data)
})
  .then(response => response.json())
  .then(data => console.log(data))
  .catch(error => console.error("Error:", error));

const axios = require('axios');

const url = "https://api.agentpmt.com/products/purchase";

const headers = {
  "Content-Type": "application/json",
  "Authorization": "Bearer ********"
};

const data = {
  product_id: "69c56fd1fd4227d57ee069c6",
  parameters: {
    "action": "find_documents",
    "database": "example_database",
    "collection": "example_collection"
  }
};

axios.post(url, data, { headers })
  .then(response => {
    console.log(response.status);
    console.log(response.data);
  })
  .catch(error => {
    console.error("Error:", error.message);
  });

Login to view your API and budget keys. The example above uses placeholder values. Sign in to see personalized code with your bearer token.

Autonomous agents can access this tool through AgentAddress credit balances or direct x402 payments. Use the Autonomous Agent API reference for endpoint shapes after choosing the access pattern below.

Recommended

Credit-Based Access Using AgentAddress

AgentAddress is preferred when an autonomous agent needs persistent file access, stored platform state, or maximum tool use ability across repeated calls.

Open Credit-Based Access Using AgentAddress

Direct x402 Payment

Use direct x402 for independent one-off tool calls that do not require shared files or stored platform state.

Accepted public payments

Stablecoin: USDC
Chains: Base, Arbitrum, Optimism, Polygon, and Avalanche

Direct x402 payments are not enabled for this product; use AgentAddress credit access instead.

Product Skill Package

This product has a published Agent Skill package. Install it when an autonomous agent needs product-specific operating instructions in its local skill registry.

Download SKILL.md View package source OpenClaw listing

OpenClaw install

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skills.sh install

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Usage Instructions

Usage guidance provided directly by the developer for this product.

MongoDB

Connect to a cloud-hosted MongoDB database and run queries, inserts, updates, deletes, aggregations, index management, Atlas Search, and Vector Search. Export results as CSV or JSON files. Supports MongoDB Atlas and any publicly accessible MongoDB instance.

Permissions

Access is controlled by a permissions array injected by the platform:

read (default if omitted): find, count, list, aggregate, distinct
write: insert, update, replace, find_one_and_update, bulk_write
delete: delete documents, find_one_and_delete
admin: indexes, search indexes, create/drop collection, run_command

Output Modes

find_documents and aggregate support an output array:

["inline"] (default): return documents in the response
["export"]: save results as a file (CSV or JSON)
["inline", "export"]: both

Use export_format to choose csv or json (default: json).

Read Actions

`find_documents`

Required: database, collection Optional: filter, projection, sort ([[field, 1/-1]]), limit (1-1000, default 20), skip, output, export_format

Example: {"action":"find_documents","database":"mydb","collection":"users","filter":{"status":"active"},"limit":10}

`count_documents`

Required: database, collection Optional: filter

`estimated_count`

Required: database, collection Fast approximate count using collection metadata (no filter support).

`list_databases`

No required params.

`list_collections`

Required: database

`aggregate`

Required: database, collection, pipeline Optional: output, export_format

Supports all pipeline stages including $search, $vectorSearch, $geoNear, $lookup, $graphLookup, $sample.

Example: {"action":"aggregate","database":"mydb","collection":"orders","pipeline":[{"$group":{"_id":"$status","count":{"$sum":1}}}]}

Vector search example: {"action":"aggregate","database":"mydb","collection":"docs","pipeline":[{"$vectorSearch":{"index":"vector_idx","path":"embedding","queryVector":[0.1,0.2,...],"numCandidates":100,"limit":10}}]}

`distinct`

Required: database, collection, field_name Optional: filter

`list_indexes`

Required: database, collection Returns all indexes on a collection with key specs, options, and names.

`list_search_indexes`

Required: database, collection Returns Atlas Search and Vector Search indexes.

Write Actions

`insert_documents`

Required: database, collection, document (single) or documents (array)

`update_documents`

Required: database, collection, update Optional: filter, upsert, many (default false)

`replace_document`

Required: database, collection, document Optional: filter, upsert

`find_one_and_update`

Atomically find and update a document, returning it. Required: database, collection, update Optional: filter, projection, sort, upsert, return_document ("before" or "after", default "before")

`find_one_and_delete`

Atomically find and delete a document, returning it. Required: database, collection Optional: filter, projection, sort

`bulk_write`

Execute mixed batch operations in a single round trip. Required: database, collection, operations

Each operation: {"operation": "insert_one|update_one|update_many|replace_one|delete_one|delete_many", ...params}

Example: {"action":"bulk_write","database":"mydb","collection":"users","operations":[{"operation":"insert_one","document":{"name":"Alice"}},{"operation":"update_one","filter":{"name":"Bob"},"update":{"$set":{"active":true}}}]}

Delete Actions

`delete_documents`

Required: database, collection Optional: filter, many (default false)

Admin Actions

`create_index`

Required: database, collection, index_keys Optional: index_name, index_options

Supports all index types: single field, compound, text ([["field","text"]]), geospatial ([["location","2dsphere"]]), hashed, wildcard.

`drop_index`

Required: database, collection, index_name

`create_search_index`

Create an Atlas Search or Vector Search index. Required: database, collection, search_index_definition Optional: index_name, search_index_type ("search" or "vectorSearch")

Vector index example: {"action":"create_search_index","database":"mydb","collection":"docs","search_index_type":"vectorSearch","index_name":"vec_idx","search_index_definition":{"fields":[{"type":"vector","path":"embedding","numDimensions":1536,"similarity":"cosine"}]}}

`update_search_index`

Required: database, collection, index_name, search_index_definition

`drop_search_index`

Required: database, collection, index_name

`create_collection`

Required: database, collection Optional: collection_options (validator, capped, timeseries, clusteredIndex, collation, expireAfterSeconds)

`drop_collection`

Required: database, collection

`run_command`

Run any MongoDB database command. Required: database, command

Example: {"action":"run_command","database":"mydb","command":{"dbStats":1}} Example: {"action":"run_command","database":"mydb","command":{"collStats":"users"}}

Notes

Filters use standard MongoDB query syntax: $eq, $gt, $lt, $in, $regex, $near, $geoWithin, etc.
Updates use $set, $unset, $inc, $push, $pull, etc.
Sort uses 1 for ascending, -1 for descending
Aggregation pipelines with $out or $merge require write permission
Max 1000 documents returned per find or aggregate call
Atlas Search and Vector Search require search indexes (use create_search_index)
Geospatial queries require 2dsphere or 2d indexes (use create_index)

Frequently Asked Questions

How do I connect this tool to an external agent?

You can install the local MCP server by opening a terminal and running:

Install commands

npm install -g @agentpmt/mcp-router
agentpmt-setup

This will connect you to local agents like Claude Code, Windsurf, Grok Build, Cursor, etc.

Alternatively you can connect to the hosted version with this config block, no installation required:

Hosted MCP config

{
  "mcpServers": {
    "agentpmt": {
      "type": "streamable-http",
      "url": "https://api.agentpmt.com/mcp",
      "headers": {
        "Authorization": "Bearer <AGENTPMT_BEARER_TOKEN>",
        "x-instance-metadata": "{\"client\":\"generic-mcp\",\"platform\":\"remote\"}"
      }
    }
  }
}

View MCP Connection Instructions for more details.

How does an external agent use this tool?

After the external agent is connected to an Agent Group that can use this tool, paste this prompt into the agent:

Agent prompt

Use the AgentPMT-Tool-Search-and-Execution tool. First call action 'get_instructions' so you know how to use the tool search interface. Then call action 'get_schema' with tool_id 69c56fd1fd4227d57ee069c6 ("MongoDB Connector"). After reading the schema and any returned instructions, tell me what this tool can do, we are going to be using it

The agent should fetch the tool schema first, collect the required parameters for your request, and then call the tool through AgentPMT.

MongoDB Connector

Available ActionsEach successful request consumes credits as outlined below.

Details

Use Cases

Dynamic MCP Setup

30 Second Setup

Hosted Streamable HTTPS

Config Example

Frequently Asked Questions

How do I connect this tool to an external agent?

How does an external agent use this tool?

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