Skip to main content
Glama
zai-one

arsenkin-mcp

by zai-one

arsenkin_submit

Queue approved paid Arsenkin requests without blocking on provider polling. Submit using profile, payload, approval ID, and idempotency key.

Instructions

Queue an approved paid Arsenkin request; never blocks on provider polling.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
payloadYes
profileYes
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

C2.3/5.0
Behavior2/5

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

With no annotations at all, the description must carry the full burden, but it only reveals one behavior: it never blocks on provider polling. It does not disclose that this action queues a paid request (potential financial side effect), whether it is idempotent, what happens on duplicate idempotency keys, or what the return value means. These are material details for an agent invoking a money-related operation.

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

Conciseness2/5

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

The phrase is short and the main purpose is front-loaded, which would be good for a simple turn. But for a tool with four required parameters and an object payload, this is under-specification rather than conciseness. It lacks any structured breakdown of the parameters or their relationships.

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

Completeness1/5

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

Given the high complexity (4 required params, nested payload, no annotations, no output schema explanation), the one-line description leaves far too much to guess. The output schema may list what is returned, but it cannot explain the approval contents, idempotency semantics, or usage context. The definition is incomplete for an agent to invocate this tool correctly.

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

Parameters1/5

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

Schema description coverage is 0% and the description adds zero information about the four required parameters. 'Payload', 'profile', 'approval_id', and 'idempotency_key' are completely unaddressed. Since the schema only provides type names and no descriptions, the agent has no way to know what to pass in, making this dimension a serious failure.

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 uses a specific verb 'Queue' and a clear resource 'approved paid Arsenkin request', which makes the primary action understandable. It also includes a behavioral nuance about non-blocking provider polling, and the 'paid/approved' qualifiers help distinguish it from prepare or estimate tools. However, it does not explicitly name any sibling tool for differentiation.

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. The mention of 'approved paid' implies a prerequisite, but the description never says to use arsenkin_prepare or arsenkin_estimate first, nor warns against using this for batch operations (arsenkin_batch_submit). The agent is left to infer the conditions from the name and one vague qualifier.

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