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MongoDB MCP Server - Provides tools for querying, inserting, updating, deleting, and aggregating documents in MongoDB databases
This MongoDB MCP server exposes 7 tools with basic but incomplete schemas and descriptions. All tools are explicitly registered with Zod schemas and descriptions are present, but they fall significantly short of production quality. Parameter descriptions are generic (e.g., 'MongoDB query filter as a JSON string' without format guidance), and output schemas are entirely undocumented. Error handling returns plain text with minimal recovery guidance. The tool names follow verb_noun convention (mongo_query, mongo_insert, etc.), which is positive, but descriptions lack the specificity needed for optimal LLM selection. No input validation guidance, no pagination support for result sets, no structured output documentation. The server accepts JSON-string parameters across all tools, which requires the LLM to manually construct JSON, a poor chat data model match (pattern:chat-data-model violation). Critical issue: destructive operations (mongo_delete) lack confirmation or dry-run patterns.
Run an aggregation pipeline on a MongoDB collection. Pass the pipeline as a JSON array of stages.
Count documents in a MongoDB collection matching an optional filter.
Delete documents from a MongoDB collection matching a filter. Returns the count of deleted documents.
Insert a single document into a MongoDB collection. Returns the inserted document ID.
List all collection names in the connected MongoDB database.
Run a find query against a MongoDB collection. Returns matching documents as JSON.
Update documents in a MongoDB collection matching a filter. Returns the count of modified documents.
All tools accept JSON as free-form strings (query, filter, update, pipeline) without format validation or LLM-facing documentation. LLMs must manually construct JSON operators ($set, $match, etc.) with zero schema guidance, inviting syntax errors.
mongo_delete tool has no confirmation, dry-run, or safety check pattern. An agent can invoke it with an empty filter {} and delete an entire collection unrecoverably. No error guidance, no undo capability.
No output schemas documented. All tool responses return plain-text content with type='text'. Responses like 'Count: 42' or 'Document inserted with ID: ObjectId(...)' are unparseable for downstream tools and offer no structured metadata (e.g., insertedId is buried in text, not a typed field). LLMs cannot reliably extract data for chaining.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 49 | 2026-07-28+ | v2 |
| 2026-03-09 | D | 51 | - | v1 |
No result pagination or limit enforcement. mongo_query has a 'limit' parameter defaulting to 10, but no description of why, no guidance on maximum safe value, and no next_cursor/total_count for pagination. A 10,000-document match could overflow context.
Parameter descriptions lack actionable constraints. 'MongoDB query filter as a JSON string' tells LLMs nothing about valid operator syntax, format errors, or examples. Descriptions should include format guidance (e.g., 'MongoDB query filter as a JSON string with operators like {$gt, $lt, $match, ...}'). Baseline expectation: 72 chars per parameter description; actual averages ~50 chars.
Error handling is minimal and non-recoverable. All tools catch errors and return 'Error executing MongoDB query: <msg>' with no classification (retryable vs. fatal), no suggested recovery steps, and no guidance on what caused the error. LLMs cannot self-correct or decide whether to retry.
No validation or permission gating. Tools accept any collection name and any filter without schema validation, SQL injection checks, or authorization checks. An agent could attempt to delete from system collections or bypass field-level access controls.
mongo_insert accepts document as union[string, object], requires LLM to guess whether to pass JSON string or JavaScript object. Schema does not constrain document structure, allowing invalid or incomplete documents to be inserted.