Skip to main content
Glama
sahilrit
by sahilrit

Get dataset items

get-dataset-items

Retrieve paginated items from an Actor run's dataset using its ID. Optionally project only specific fields to reduce response size.

Instructions

Page through the results of a previous Actor run. Use the datasetId returned by call-actor. Use fields to project only the keys you need — this matters when items contain long text such as page content or job descriptions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
fieldsNoOnly return these top-level keys.
offsetNo
datasetIdYes

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden for behavioral disclosure. It communicates that the operation is a read/pagination action and adds a useful performance-oriented note about using fields for long-text items. However, it does not describe the response shape, empty-result behavior, or any paging semantics beyond the schema defaults.

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?

Two tight sentences: the first states the operation and the datasource, the second gives actionable projection advice. No filler, and the most important usage context is front-loaded.

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

Completeness4/5

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

For a simple paginated read tool without an output schema, the description is nearly sufficient: it names the source of datasetId, explains pagination, and warns about large payloads. The main gap is that it never explicitly states the return format, though 'page through' and 'items' strongly imply a paginated list of objects.

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 only 25%, so the description must compensate. It does add real meaning to datasetId ('returned by call-actor') and fields ('project only the keys you need'). But limit and offset are not semantically explained in the description; the agent must rely on schema defaults and names to infer their role.

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 ('Page through') and resource ('results of a previous Actor run'), clearly separating this from sibling tools that manage actor runs or datasets themselves. It also grounds the datasetId in the output of call-actor, which makes the tool's role in the workflow unambiguous.

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 gives clear context: this tool is for paging through items after an Actor run, and the datasetId should come from call-actor. It does not explicitly name alternatives or state when not to use this tool, so it stops short of a 5, but the intended usage is clear.

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