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LightWorker

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Python
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approve_task

Releases a task awaiting approval into the execution queue after validating its task ID, approval ID, and scope digest.

Instructions

Release one task that is awaiting approval into the execution queue.

Input Schema

NameRequiredDescriptionDefault
task_idYes
approval_idYes
scope_digestYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.8.0
    • addedInput schema / properties / approval_id
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / scope_digest
      Added value: +{
+  "pattern": "^[a-f0-9]{64}$",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "task_id"
-]New value: +[
+  "task_id",
+  "approval_id",
+  "scope_digest"
+]
  2. First observedv0.1.1

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already disclose that this is a non-read-only, non-destructive, closed-world operation. The description adds that the task is moved into the execution queue, but it does not explain approval requirements, idempotency, failure behavior, or what happens to the approval record.

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 definition is a single front-loaded sentence with no filler. It is concise and directly states the action and effect.

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?

For a mutation tool with three required opaque parameters, no output schema, and no parameter descriptions, the description is too sparse. It does not provide enough context to invoke the tool confidently beyond knowing its queue effect.

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?

All three required parameters have 0% schema description coverage, and the description does not explain the meaning or role of task_id, approval_id, or scope_digest. The digest pattern and approval relationship are especially opaque.

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 and effect: it releases one task awaiting approval into the execution queue. It is clear what the tool does, but it does not explicitly distinguish itself from siblings such as escalate_task, retry_fallback, or cancel_task.

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

Usage Guidelines3/5

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

The phrase 'awaiting approval' implies the condition for use, but the description does not say when to choose this tool over alternatives, nor does it state exclusions or prerequisites.

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

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