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llm-cli-gateway

llm-cli-gateway

by verivus-oss
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TypeScript
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llm_job_status

Read-onlyIdempotent

Check lifecycle status and privacy-safe normalized progress of an async or deferred-sync job by jobId, with sequence-bounded paging for long-running workloads.

Instructions

Check lifecycle status and bounded privacy-safe normalized progress for a gateway async or deferred-sync job by jobId.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "jobId": "<jobId>"
    },
    "name": "llm_job_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
jobIdYesAsync job ID from *_request_async
progressLimitNoMaximum next normalized progress events to return in forward sequence order
afterProgressSeqNoReturn normalized progress events with a sequence greater than this value

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv3.2.0
  2. Removedv3.0.0
  3. First observedv2.16.0

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already establish readOnly, idempotent, non-destructive behavior, so the bar is lower. The description adds that progress is normalized, bounded, and privacy-safe, but it does not explain lifecycle semantics, retention, or error behavior. This is useful but not rich context.

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?

A single, well-ordered sentence places the action and resource first, then qualifies the response. Every phrase ('lifecycle status', 'bounded', 'privacy-safe', 'normalized progress') earns its place without repetition.

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?

Given full schema parameter documentation and annotations covering the safety profile, the description is mostly sufficient: it states what is returned (lifecycle status and normalized progress). The only gap is lack of explicit guidance for choosing among the closely related job_status, llm_job_watch, llm_job_result, and llm_request_result siblings.

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 description coverage is 100%, and all three parameters are individually documented (jobId source, progressLimit bound, afterProgressSeq cursor). The description does not need to compensate, and it does not add parameter detail beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Check') and names the resource precisely: lifecycle status and normalized progress for a gateway async or deferred-sync job by jobId. This separates it from generic job_status and result-retrieval siblings by emphasizing normalized, bounded progress rather than raw results.

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 description implies the use case—querying by jobId to poll status/progress on async or deferred-sync jobs—but never names alternatives or states when not to use it. With many siblings like llm_job_watch, llm_job_result, and job_status, explicit routing would be stronger.

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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