Skip to main content
Glama

batch_status

Monitor and manage batch processing jobs: retrieve status by job ID, cancel running jobs, and list active or recent jobs.

Instructions

Deprecated. Prefer convert(mode="batch_dir"). Still registered this minor version.

Monitor and manage batch processing jobs.

Modes:

status Get the status of a specific batch job. Parameters: job_id (required)

cancel Cancel a running batch job. Parameters: job_id (required)

active List all currently active batch jobs. Parameters: none

recent List recent batch jobs. Parameters: limit, status

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
limitNo
job_idNo
statusNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observedv0.0.4

TDQS

A4.4/5.0
Behavior3/5

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

With no annotations, the description carries the burden of behavior. It discloses deprecation status and the supported operations, and the cancel mode implies mutation, but it does not describe side effects, reversibility, permissions, or behavior beyond the mode names. Some added context exists, but not deep behavioral transparency.

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 deprecation warning is front-loaded, followed by a terse mode-by-mode breakdown. The markdown structure makes the conditional parameter requirements easy to scan, and there is no filler or repetition.

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?

The description covers all modes and their parameters, deprecation, and the preferred alternative. The output schema exists, so return values need not be spelled out, but the status filter values and mode-specific edge behavior are not specified, leaving minor gaps for an agent.

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?

Input schema descriptions are absent (0% coverage), but the description compensates by mapping each mode to its relevant parameters and marking job_id as required for status and cancel. It does not define allowed values for status or clarify limit semantics, so it is helpful but not exhaustive.

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 clearly identifies the tool as managing batch processing jobs, then enumerates four distinct modes (status, cancel, active, recent), each with a specific action and resource. It also differentiates itself from its sibling convert by stating that convert(mode="batch_dir") is preferred.

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

Usage Guidelines5/5

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

It explicitly marks the tool as Deprecated, instructs the agent to prefer convert(mode="batch_dir"), and notes it is still registered this minor version. This gives a clear when-not-to-use signal and names the alternative, while the mode list clarifies internal usage.

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/AnnasMazhar/pyspark_mcp'

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