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data-analysis-agent

data-analysis-agent

by Soham-Donode
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

list_sessions

List all active in-memory and saved dataset sessions, including session ID, dataset name, shape, memory, and status.

Instructions

List all dataset sessions (both active in-memory and saved on disk), including their session_id, dataset name, shape, memory, and status.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_sessions"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It clearly indicates the operation is a non-destructive enumeration and adds useful context about covering both active in-memory and saved sessions, plus the returned fields. It stops short of explicitly saying it causes no side effects, but 'List' strongly implies read-only behavior.

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

Conciseness5/5

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

A single, well-structured sentence front-loads the action and resource, then adds relevant scoping details and field names without any filler. Every element contributes to understanding the tool.

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

Completeness5/5

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

Given that the tool takes no parameters and an output schema exists, the description is fully sufficient. It covers what is listed, the scope (in-memory and disk), and the output fields, leaving no meaningful gap for correct invocation.

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

Parameters4/5

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

The tool has zero parameters, so there are no semantics to add beyond the empty schema. The baseline of 4 applies, and the description correctly focuses on output scope rather than inventing parameter guidance.

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

Purpose5/5

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

The description uses a specific verb ('List') and a clear resource ('all dataset sessions'), explicitly covering both active in-memory and saved-on-disk sessions. It also enumerates the included fields, making the tool's purpose unambiguous and distinct from sibling tools like restore_session or load_dataset.

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

Usage Guidelines4/5

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

The description makes it clear that this is the tool to use when enumerating sessions across both in-memory and disk state. It does not explicitly name alternatives or exclusions, but the listing scope is stated clearly enough to guide selection among the siblings.

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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