Skip to main content
Glama

MadeCheck

Decide pending action

decide_pending_action

Decide pending action. Decide pending_action_id with decision=approve or reject and request_id as the person; returns the decision or executed action result after current-value checks. not available to AI agents. 处理待本人点的请求。本人用 pending_action_id、decision=approve/reject 和 request_id 处理请求;核对当前值后返回决定或动作执行结果。AI 不能调用。

Input Schema

NameRequiredDescriptionDefault
decisionYes
request_idYes
pending_action_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

The annotations already declare that this is not read-only, not idempotent, and not destructive. The description adds that the result follows current-value checks and may return either the decision or an executed action result, plus the AI exclusion. It does not describe approval side effects in detail, but it provides meaningful behavioral context beyond the annotations.

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

Conciseness3/5

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

The English and Chinese text duplicate the same information, roughly doubling length without adding new semantics. The content is front-loaded and useful, but the bilingual repetition hurts conciseness.

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?

There is no output schema, and the description explains that the return is either the decision or the executed action result. Combined with annotations and the explicit AI exclusion, this is largely complete for a three-parameter mutation tool, though the meaning of current-value checks remains unspecified.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate, and it does: it identifies pending_action_id, decision=approve/reject, and request_id as the person identifier. It explains all three required parameters and supplies the key decision enum values.

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 action (decide a pending action) and names the key inputs and decision values. It remains somewhat internal in that it does not explain what a pending action represents, but it is distinguishable from the sibling list_pending_actions tool.

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 says the tool is not available to AI agents and is for the person themselves to handle requests. That is a clear when-not-to-use condition, which is especially valuable for agent selection.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources