Skip to main content
Glama
zai-one

arsenkin-mcp

by zai-one

arsenkin_batch_submit

Submit approved SEO batch jobs for processing. Queues requests with approval and idempotency keys, while a central worker consolidates and polls for results.

Instructions

Queue one approved batch job; worker coalesces and polls centrally.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestsYes
approval_idYes
idempotency_keyYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observedv0.2.0

TDQS

C2.9/5.0
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It does reveal that the tool queues rather than immediately executes, and that a worker handles coalescing and polling, which is useful. However, it does not mention side effects, idempotency behavior, failure modes, or what the output represents.

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

Conciseness4/5

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

The description is a single front-loaded sentence with no filler words. It conveys the core action and a notable behavioral trait in a compact form, though it is short enough that some useful context is omitted.

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

Completeness2/5

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

Despite having an output schema, the description does not clarify what a successful submission returns or how the queue workflow progresses. Three required parameters are undocumented, and there is no guidance on idempotency, approval validation, or error handling, leaving an agent under-equipped to call this tool correctly.

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

Parameters2/5

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

Schema description coverage is 0%, so the description needed to compensate, but it does not explain any of the three required parameters. The phrase 'approved batch job' loosely maps to approval_id, and the name suggests idempotency_key's purpose, but the description adds no concrete parameter-level meaning beyond what the bare property names imply.

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 ('Queue') and resource ('one approved batch job'), and adds a distinguishing behavioral note about central worker coalescing and polling. However, it does not clearly differentiate from the sibling 'submit' tools or explain what 'approved' means in this workflow.

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 explicit guidance on when to use this tool versus alternatives like arsenkin_submit, arsenkin_batch_prepare, or arsenkin_get_result. The phrase 'approved batch job' implies a prerequisite, but the description never states when to choose this over sibling tools or what conditions must be met.

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/zai-one/arsenkin-mcp'

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