Skip to main content
Glama
sahilrit
by sahilrit

Get dataset metadata

get-dataset

Fetch the item count of a dataset without reading its contents, so you can quickly assess dataset size before processing.

Instructions

Get the item count of a dataset without reading the items themselves.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetIdYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses a useful behavioral trait: the operation does not read the underlying items, implying it is lightweight and non-invasive. However, it does not describe return behavior, error cases, authorization requirements, or any other side effects, leaving much undisclosed.

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?

The description is a single focused sentence that front-loads the core purpose ('item count') and adds the key differentiator without any wasted words. It earns its place and remains memorable.

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 tool, the description gives the essential function, but it lacks an output schema and does not describe what the response contains (e.g., a bare integer or an object with a count field). It also does not mention sibling relationships or edge cases, leaving some uncertainty for an agent, though the overall complexity is low.

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 compensate for parameter documentation. The only parameter, datasetId, is self-explanatory from its name and the resource context, and the description's mention of 'dataset' loosely ties to it. Still, no format, validation, or usage detail is added beyond the schema, but the parameter is simple enough that this is acceptable.

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 states a specific verb ('Get'), a resource ('dataset'), and a precise scope ('item count'), and distinguishes the tool from siblings like get-dataset-items by noting it does not read the items. However, the title 'Get dataset metadata' implies a broader purpose than the description's narrow 'item count' focus, creating mild ambiguity.

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

Usage Guidelines3/5

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

The phrase 'without reading the items themselves' implies that this tool is appropriate when only a count is needed, and not when item content is required. But it does not name alternatives such as get-dataset-items or get-dataset-schema, nor does it state explicit when-to-use or when-not-to-use conditions.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sahilrit/openactors'

If you have feedback or need assistance with the MCP directory API, please join our Discord server