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LS-PrePost-MCP

LS-PrePost-MCP

by lwz20210407
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

read_job

Retrieve a recorded job's status, errors, log paths, and validated artifacts by ID to verify outcomes and diagnose failures.

Instructions

Read a recorded task including status, errors, log paths and validated artifacts.

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": {
      "job_id": "<job_id>"
    },
    "name": "read_job"
  }
}

Input Schema

NameRequiredDescriptionDefault
job_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It usefully discloses the returned content (status, errors, log paths, artifacts), which doubles as a partial output contract in the absence of an output schema, but says nothing about failure behavior for a missing/invalid job_id or any auth/permission requirement.

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

Conciseness4/5

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

A single front-loaded sentence with no filler; the verb and the payload contents appear immediately. It could be slightly richer without bloat, but nothing is wasted.

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

Completeness3/5

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

For a one-parameter read tool with no annotations and no output schema, the description covers the return shape reasonably but omits the input's provenance and any error/edge behavior. Adequate but with clear gaps given the structured fields provide no backup.

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

Parameters2/5

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

There is exactly one parameter, job_id, with 0% schema description coverage, so the description must compensate. It never mentions the id or its expected form (e.g., where the id comes from, as returned by list_jobs), leaving parameter meaning entirely undocumented.

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?

States a specific verb (read) and resource (a recorded task/job), and enumerates what the read yields: status, errors, log paths, validated artifacts. This contrasts with the sibling list_jobs, though that distinction is implied rather than stated.

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?

No when-to-use guidance, no exclusions, and no mention of list_jobs as the alternative for enumerating jobs. The agent must infer that this is for a single job given an id, purely from the name and parameter.

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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