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Vijayakumar-DevOps

MongoDB MCP Server

aggregate-db

Read-only

Execute a MongoDB aggregation pipeline on a database to process and analyze data. Supports database-level stages like $changeStream, $currentOp, $queryStats.

Instructions

Run an aggregation against a MongoDB database

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
databaseYesDatabase name
pipelineYesAn array of aggregation stages to execute. The first stage must be a database-level aggregation stage (one of `$changeStream`, `$currentOp`, `$documents`, `$listLocalSessions`, `$queryStats`). https://www.mongodb.com/docs/manual/reference/mql/aggregation-stages/#db.aggregate---stages
responseBytesLimitNoThe maximum number of bytes to return in the response. This value is capped by the server's configured maxBytesPerQuery and cannot be exceeded.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
documentsYesThe documents returned by the aggregation pipeline
appliedLimitsYesThe limits applied to the aggregation pipeline
aggResultsCountNoThe total number of documents returned by the aggregation pipeline
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations already provide readOnlyHint=true and destructiveHint=false, and the description adds no additional behavioral context. It does not mention that only database-level stages are allowed, the response size cap, or any potential side effects. Despite the annotations, the description is essentially a restatement of the tool name with no value beyond the structured metadata.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with no wasted words. It is concise, though it omits important details; however, that omission is better captured in other dimensions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is too sparse for a tool with an aggregation pipeline. It does not mention that this is database-level aggregation (which would differentiate it from 'aggregate'), nor does it highlight constraints like the allowed stages or response limits. The schema covers some of this, but the description fails to provide the necessary context for correct tool selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the schema itself thoroughly documents each parameter, including a link for valid stages. The description adds no extra semantic meaning, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Run an aggregation') and the resource ('a MongoDB database'), which is specific enough. However, it does not explicitly distinguish itself from the sibling tool 'aggregate', so it gets a 4 rather than a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The sibling tool 'aggregate' likely handles collection-level aggregations, but this is not mentioned. The description offers no exclusions or explicit usage scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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