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AI-ML-Lab-MCP

AI-ML-Lab-MCP

by SyedHassan904
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Memory Usage

memory_usage

Report memory consumption for a dataset and each of its columns to identify high-memory fields and guide dataset optimization.

Instructions

Report memory consumption of the dataset and its columns. Args: dataset_id (str): ID of the dataset. Returns: dict: col_memory_usage_in_mb (per column, including the index) and dataset_memory_usuage_in_mb (total).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset_id": "<dataset_id>"
    },
    "name": "memory_usage"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full burden; it does establish the operation is a read-only report and it discloses the shape of the returned dict, which is valuable since there is no output schema. However, it says nothing about units consistency, whether memory is deep or shallow, or how the index is treated when included, and the total key is misspelled as 'dataset_memory_usuage_in_mb', which could cause an agent to look up a key that does not exist.

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

Conciseness3/5

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

The docstring is short and front-loads purpose before Args/Returns, which is good. But the Args block only echoes schema information, and the misspelled return key plus boilerplate 'Returns: dict:' framing cost space without adding reliability.

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

Completeness3/5

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

For a single-parameter read tool with no output schema, the description does the important work of enumerating return keys, so an agent can use the result without guessing. It falls short on behavioral detail (units, index handling, large-dataset behavior) and on the accuracy of the documented key name.

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 0%, so the description must carry parameter meaning. It does document the single parameter as 'ID of the dataset' with a str type, which is the minimum needed to invoke correctly, but the parenthetical restates the name and type already visible in the schema without clarifying ID format or where such an ID comes from.

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 opens with a specific verb and resource: 'Report memory consumption of the dataset and its columns', which tells an agent exactly what is measured and at what granularity (dataset + per-column). It does not name or differentiate itself from near neighbors like dataset_info or dataset_shape, so the agent still has to infer which profiling tool to pick.

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?

There is no guidance on when to call this versus alternatives such as dataset_info or describe_dataset, and no stated preconditions (e.g., dataset must exist or be loaded). The only usage signal is the required dataset_id argument, which is inferred rather than stated.

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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