Skip to main content
Glama
zai-one

arsenkin-mcp

by zai-one

Arsenkin: batch submit

arsenkin_batch_submit
DestructiveIdempotent

Queue an approved batch job for later processing; requires approval_id and idempotency_key, then poll task status to track results.

Instructions

Queue an approved batch job; the worker coalesces and polls. Use after explicit OK; poll arsenkin_task_status. Cost: spends Arsenkin limits (see estimate); requires approval_id from arsenkin_prepare.

Input Schema

NameRequiredDescriptionDefault
requestsYes
approval_idYes
idempotency_keyYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare destructive/openWorld/idempotent, so the bar is lower, yet the description adds real value: it discloses the cost profile ('spends Arsenkin limits'), the approval gate, and the coalescing/polling async behavior. It does not say what the queued job destroys or how failures surface.

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?

Compact and front-loaded: the primary action leads, then the workflow, then cost/prereqs. Semicolon-separated clauses are dense but each carries distinct information, with no filler.

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 an async, destructive, paid batch submission with an output schema available, the description covers the essentials: approval prerequisite, cost, next-step polling, and upstream tool. The uncovered gap is the contents of the requests payload.

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 coverage is 0%, so the description must carry the load. It explains where approval_id comes from ('from arsenkin_prepare'), which is genuinely useful, but says nothing about the shape of the requests array or the role of idempotency_key beyond what idempotentHint implies. Partial compensation only.

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?

States a specific verb and resource ('Queue an approved batch job') and the async execution model ('the worker coalesces and polls'). It implicitly contrasts with the single-job sibling arsenkin_submit via 'batch', but never names that alternative explicitly, so an agent must infer the distinction.

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?

Gives a clear precondition ('Use after explicit OK') and a concrete next step ('poll arsenkin_task_status'), plus the upstream dependency on arsenkin_prepare. It stops short of naming when NOT to use this versus arsenkin_submit, so there is no explicit exclusion guidance.

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