Loading ToolBench
A MongoDB MCP (Model Context Protocol) server written in Go that provides tools for MongoDB database operations including collection management, document CRUD, indexing, and ID generation.
This MongoDB MCP server has significant structural and documentation gaps. While 10 tools are registered with basic schemas, most lack depth in parameter descriptions and output schema documentation. Tool naming has some issues (e.g., 'InertOne' is a typo for 'InsertOne'; 'entity_id_generator' does not follow verb_noun convention). Descriptions are present but minimal (10-60 chars for most), well below the 50-200 char optimum for LLM-usable tool specs. Parameter types are visible in code but descriptions are sparse or generic. Output schemas are not documented, handlers return text responses with no structure documented for the LLM. Error handling is minimal: most tools return error strings with no recovery guidance or classification. No input validation or sanitization is evident. No permission gates or audit logging. Security risks include MongoDB injection vulnerability in filter/update parameters (mapstructure.Decode on user input without schema validation). Composition is reasonable, tools have single responsibilities, but the lack of structured output breaks tool chaining (e.g., Find returns text, making downstream use difficult).
Count documents in a collection using MongoDB query syntax
create index in mongodb
Delete a single document into a collection
drop index in mongodb
Query documents in a collection using MongoDB query syntax
Insert a single document into a collection
List all collections in mongodb
Tool naming error: 'InertOne' is a typo and does not follow verb_noun convention. Should be 'InsertOne' or 'insert_one'.
Tool 'entity_id_generator' does not follow verb_noun naming convention. LLMs struggle with non-verb-prefixed names. Rename to 'generate_entity_id' or 'create_entity_id'.
All tool descriptions are extremely brief (10-60 characters), well below the 50-200 character recommendation. Descriptions lack WHAT the tool does, WHEN to use it, and WHAT it returns. Example: 'List all collections in mongodb' provides no context on when to call it or what the output structure is.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 43 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 37 | - | v1 |
List all indexes in mongodb
update a single document into a collection
generate id for different type entity
Output schemas are not documented. All tools return mcp.NewToolResultText() with unstructured string responses. The LLM has no way to know what fields or structure to expect, breaking downstream tool chaining and forcing parsing of text output.
Parameter descriptions are missing or generic. Example: 'filter' parameter in Find is described as 'MongoDB query filter' with no format constraints, range, or examples of valid filter syntax. LLMs cannot infer valid MongoDB query syntax from this.
No input validation or sanitization. Filter and update parameters accept raw objects via mapstructure.Decode without schema validation. Vulnerable to MongoDB injection attacks (e.g., filter: {$where: 'malicious code'}).
Error handling is minimal and non-actionable. All tools return bare error strings (e.g., err.Error()) with no classification (retryable vs fatal), no recovery guidance, and no context on what went wrong. Example: 'connection refused' tells the LLM nothing about whether to retry or ask the user.
Destructive tools (DeleteOne, DropIndex) lack confirmation or dry-run patterns. No mechanism to prevent accidental deletion. Agents can invoke these irreversibly without safeguards.
No pagination or result limiting. Find tool has a 'limit' parameter (max 1000) but no offset/page parameters and no documentation on how to retrieve large result sets. ListCollections and ListIndexes offer no pagination at all.
No permission gates or audit logging. Tools operate with the MongoDB credentials baked into the server process, no per-user authorization, no rate limiting, no access logging, no compliance trail.
DropIndex parameter 'index_name' is typed as 'object' instead of 'string'. This is a schema error, index names are strings, not objects. Will cause type confusion in the LLM.