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llm-cli-gateway

llm-cli-gateway

by verivus-oss
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

job_result

Read-onlyIdempotent

Retrieve normalized provider output from a validation job, giving standardized results instead of raw request data.

Instructions

Collect a VALIDATION job's normalized provider output — distinct from llm_job_result, which returns raw provider request job output.

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": "job_result"
  }
}

Input Schema

NameRequiredDescriptionDefault
jobIdYesValidation job ID.
maxCharsNoMaximum result size.
providerNoProvider that produced the job, used for normalized validation output.

Schema Changelog

Changes observed during successful MCP inspections.

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

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the important normalized-vs-raw distinction but does not disclose lifecycle requirements, error behavior, or pagination/truncation behavior. This is acceptable but not rich.

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 description is a single, front-loaded sentence with no filler. It states the core behavior first and then adds the distinguishing contrast with llm_job_result. Every part earns its place.

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 the strong annotations, complete parameter descriptions, and straightforward retrieval nature, the description is mostly sufficient. It clarifies the output category (normalized provider output) but does not describe the exact return shape or whether the job must be completed before calling; this is a minor gap.

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%, so each parameter is already documented in the schema. The description re-emphasizes that this is a validation job result and mentions 'normalized provider output', but it does not add significant meaning beyond what the parameter descriptions provide. Baseline 3 is appropriate.

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 ('Collect') and resource ('a VALIDATION job's normalized provider output'), making the tool's function clear. It explicitly distinguishes itself from llm_job_result, so an agent can tell them apart without inspecting schemas.

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?

The description names the relevant alternative (llm_job_result) and explains the key distinction: normalized validation output vs raw provider request job output. This gives the agent a clear selection rule for choosing between the two tools.

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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