| name | mongodb-connector |
| description | MongoDB Connector: Connect to a user's cloud-hosted MongoDB instance and run 24 permission-gated database operations. Use when an agent needs mongodb connector, query mongodb collections, export data as csv or json, run aggregation pipelines, atlas vector search for rag, aggregate, database, collection through AgentPMT-hosted remote tool calls. Discovery terms: mongodb connector, query mongodb collections, export data as csv or json, run aggregation pipelines, atlas vector search for rag. |
| version | 1.0.0 |
| homepage | https://www.agentpmt.com/marketplace/mongodb-connector |
| compatibility | Agent instructions for AgentPMT-hosted remote tool calls. Follow this skill body for supported account, wallet, and setup routes. No local command runtime is declared. |
| metadata | {"author":"agentpmt","openclaw":{"homepage":"https://www.agentpmt.com/marketplace/mongodb-connector"}} |
MongoDB Connector
Freshness
Last updated: 2026-06-09.
If the current date is more than 7 days after the last updated date, reinstall this skill from skills.sh or ClawHub before relying on endpoints, schemas, setup steps, or examples.
What This Tool Does
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.
Product Instructions
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, distinctwrite: insert, update, replace, find_one_and_update, bulk_writedelete: delete documents, find_one_and_deleteadmin: 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)
When To Use
- Use this skill for
MongoDB Connector on AgentPMT.
- Use it when an agent needs this specific tool's behavior, schema, inputs, outputs, and invocation shape.
- Search and activation keywords: mongodb connector, query mongodb collections, export data as csv or json, run aggregation pipelines, atlas vector search for rag, aggregate, database, collection.
- Supported action names:
aggregate, bulk_write, count_documents, create_collection, create_index, create_search_index, delete_documents, distinct, drop_collection, drop_index, drop_search_index, estimated_count, find_documents, find_one_and_delete, find_one_and_update, insert_documents, list_collections, list_databases, list_indexes, list_search_indexes, replace_document, run_command, update_documents, update_search_index.
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
Related Product Skills
Categories And Industries
No categories or industry tags are published for this tool.
Actions And Schema
Complete generated action schema: ./schema.md.
Supported action count: 24.
x402 availability: not enabled for this product.
aggregate (action slug: aggregate): Run an aggregation pipeline. Supports $search, $vectorSearch, $geoNear, $lookup, CSV/JSON export. Price: 5 credits. Parameters: collection, database, export_format, output, pipeline.bulk_write (action slug: bulk-write): Execute mixed batch of insert, update, replace, and delete operations Price: 5 credits. Parameters: collection, database, operations.count_documents (action slug: count-documents): Count documents matching a filter Price: 5 credits. Parameters: collection, database, filter.create_collection (action slug: create-collection): Create a collection with optional schema validation, capping, or time series config Price: 5 credits. Parameters: collection, collection_options, database.create_index (action slug: create-index): Create an index (single, compound, text, geospatial, hashed, wildcard) Price: 5 credits. Parameters: collection, database, index_keys, index_name, index_options.create_search_index (action slug: create-search-index): Create an Atlas Search or Vector Search index Price: 5 credits. Parameters: collection, database, index_name, search_index_definition, search_index_type.delete_documents (action slug: delete-documents): Delete one or many documents Price: 5 credits. Parameters: collection, database, filter, many.distinct (action slug: distinct): Get distinct values for a field Price: credits. Parameters: , , , .
Live Schema And Examples
Use the compact schema above for ordinary calls. Before a new production integration, or whenever parameters, enum values, nested objects, outputs, or examples are unclear, fetch live details first.
- Exact schema: call
agentpmt-tool-search-and-execution with action: "get_schema", and tool_id: "mongodb-connector".
- Detailed examples: call
agentpmt-tool-search-and-execution with action: "get_instructions" and tool_id: "mongodb-connector", or call this product with action: "get_instructions" when the product tool is already selected.
- Treat returned live schema and instructions as more specific than this generated summary.
MCP schema lookup through the main AgentPMT MCP server:
{
"method": "tools/call",
"params": {
"name": "AgentPMT-Tool-Search-and-Execution",
"arguments": {
"action": "get_schema",
"tool_id": "mongodb-connector"
}
}
}
For live examples, keep the same MCP tool and use these arguments:
{
"action": "get_instructions",
"tool_id": "mongodb-connector"
}
Authenticated AgentPMT REST schema lookup body:
{
"name": "agentpmt-tool-search-and-execution",
"parameters": {
"action": "get_schema",
"tool_id": "mongodb-connector"
}
}
Authenticated AgentPMT REST live examples body:
{
"name": "agentpmt-tool-search-and-execution",
"parameters": {
"action": "get_instructions",
"tool_id": "mongodb-connector"
}
}
Call This Tool
Product slug: mongodb-connector
Marketplace page: https://www.agentpmt.com/marketplace/mongodb-connector
- AgentPMT account route: first use
../agentpmt-account-mcp-rest-api-setup to connect the main MCP server or REST API for an Agent Group where this tool is enabled.
- x402 route: not enabled for this product.
- AgentPMT overview: use
../what-is-agentpmt for marketplace, Agent Group, workflow, MCP, REST, and payment concepts.
If those setup skills are not installed beside this product skill, use the downloads below.
Core AgentPMT setup skills:
- What AgentPMT is: ../what-is-agentpmt
- AgentPMT account MCP/REST setup: ../agentpmt-account-mcp-rest-api-setup
skills.sh install script:
npx skills add AgentPMT/agent-skills --skill what-is-agentpmt
npx skills add AgentPMT/agent-skills --skill agentpmt-account-mcp-rest-api-setup
MCP call shape after the main AgentPMT MCP server is connected:
{
"method": "tools/call",
"params": {
"name": "MongoDB-Connector",
"arguments": {
"action": "aggregate",
"collection": "example collection",
"database": "example database",
"export_format": "json",
"output": [
"inline"
],
"pipeline": [
{}
]
}
}
}
Use the exact tool name returned by tools/list; the name above is the expected readable form.
Authenticated AgentPMT REST call body:
{
"name": "mongodb-connector",
"parameters": {
"action": "aggregate",
"collection": "example collection",
"database": "example database",
"export_format": "json",
"output": [
"inline"
],
"pipeline": [
{}
]
}
}
Use the setup skill for the account connection details before making REST calls.
Response Handling
- Treat the returned JSON as the source of truth for this tool call.
- If the response includes warnings or correction targets, apply them before retrying.
- If the response includes a
passed or success-style boolean, use it as the workflow gate.
- If validation fails or the response shape is unclear, call
get_schema or get_instructions before retrying.
- If
aggregate fails, preserve the request parameters and retry only after fixing schema, auth, or payment errors.
Security
- Do not place account secrets, wallet private keys, mnemonics, signatures, or payment headers in prompts or logs.
- Keep tool inputs scoped to the minimum content needed for the task.
- Use the setup skills for credential handling; this product skill only defines product-specific behavior.
AgentPMT Reference
