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MCP server for comprehensive MongoDB integration: database operations, collections, aggregation, queries, connection management, monitoring
The MongoDB MCP server has 21 tools with broadly functional definitions. All tools have names, descriptions, and input schemas visible in the source code. However, there are systematic gaps in description quality, parameter documentation, output schema definition, and error handling guidance. Most descriptions are adequate but generic (average ~100-150 chars), and while parameters have type declarations, many lack actionable format guidance. The server follows basic tool patterns but misses several production-grade refinements around error recovery, output schema documentation, and LLM-optimized descriptions. No evidence of Zod schema definitions being exposed as output contracts to the LLM context.
Run an aggregation against a MongoDB collection
Describe the indexes for a collection
Describe the schema for a collection by sampling documents
Get storage size of a specific collection
Establish connection to MongoDB using connection string from environment variable MONGODB_MCP_CONNECTION_STRING. Call service-info first to check current connection status.
Gets the number of documents in a MongoDB collection using db.collection.count() and query as an optional filter parameter
Create a new collection in a MongoDB database. Use for: Creating new collections with optional configuration.
Output schemas not documented in tool definitions. Tools like find(), aggregate(), count() return results but LLM has no schema contract describing field structure, pagination info, or response envelope. Agents cannot reliably extract nested data or chain results to downstream tools.
Descriptions lack actionable error recovery guidance. When a tool fails (e.g., 'collection not found'), the LLM has no instruction on which tool to call next or how to recover. Error responses use a generic toolError() function that returns name/message, but descriptions do not guide recovery paths.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 59 | <=2025-11-25 | v2 |
Create an index on a MongoDB collection. Use for: Improving query performance by creating indexes on collection fields.
Get statistics for a specific database
Delete one or multiple documents from a MongoDB collection. Use for: Removing records from MongoDB collections. Bulk deletes (multi=true) require an explicit confirmation literal.
Disconnect from MongoDB and clear the connection. Use service-info to check connection status after disconnecting.
Drop a collection from a MongoDB database. Use for: Removing collections that are no longer needed. Requires the confirmation literal to be passed explicitly to prevent accidental data loss.
Drop an index from a MongoDB collection. Use for: Removing indexes that are no longer needed or causing performance issues. Requires the confirmation literal to be passed explicitly.
Get execution plan and statistics for a query
Query documents in a MongoDB collection
Insert documents into a MongoDB collection. Use for: Adding new records to MongoDB collections
List all collections in a specific database
List all databases on the MongoDB server
Retrieve MongoDB server logs
Get service information including connection status, supported operations, and server version
Update documents in a MongoDB collection. Use for: Modifying records in MongoDB collections. Bulk updates (multi=true) require an explicit confirmation literal.
Pagination not explicitly supported in result-returning tools (find, aggregate, count, mongodb-logs). Descriptions do not mention result limits or how to handle large result sets. No explicit total_count or next_cursor in output contracts. Risk of context window exhaustion.
Confirmation flow for destructive operations is not idempotent. delete and drop-collection require a confirmation string literal, but confirmation mechanism is not enforced by schema validation. If an agent passes an incorrect confirmation, the tool must error; current implementation likely succeeds only if exact match. No idempotent or dry-run variant provided.
Parameter constraints not specified in descriptions. Examples: find() has no documented max limit; aggregate() has no documented pipeline size limits; collection-schema has no documented range for sampleSize. LLMs will pass unconstrained values, risking timeouts or OOM.
Tool names use hyphens (e.g., 'list-databases', 'collection-storage-size') instead of underscores (list_databases, collection_storage_size). While functional, snake_case is more widely adopted in production LLM tools and easier for LLMs to tokenize and parse. This is a minor stylistic issue but affects consistency with 90% of production tools.
Descriptions for discovery tools (list-databases, list-collections) do not explain when to call them or what to do with results. Missing: 'Call this first to discover available resources before querying.' This guidance helps LLMs build multi-step plans.