LS-PrePost-MCP

by lwz20210407

Overview Schema Source Related Servers Discussions Admin

Python

Local

list_parts

Retrieve native user part IDs and optional names from an LS-PrePost model; null names indicate unavailable bindings.

Instructions

List native user part IDs and optional names; null names indicate an unavailable binding.

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": {
      "model": "<model>"
    },
    "name": "list_parts"
  }
}

Input Schema

Name	Required	Default
`limit`	No
`model`	Yes
`file_type`	No	keyword

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 30, 2026

TDQS

C2.9/5.0

Behavior3/5

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

With no annotations the description carries the full burden. It usefully discloses that null names signal an unavailable binding, which is real behavioral information about the output, but says nothing about read-only semantics, pagination via limit, or what the required model argument expects.

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 the core purpose stated first and no filler. It is appropriately sized, though brevity comes at the cost of the missing detail noted elsewhere.

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?

No output schema, no annotations, and three parameters at 0% schema coverage. The one useful disclosure (null-name semantics) does not compensate for the absent parameter and usage information an agent needs.

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?

Schema description coverage is 0%, so the description must compensate, yet it explains none of the three parameters (model, limit, file_type). The distinction between native IDs and optional names hints at the domain but gives no parameter-level meaning.

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 and resource ('List native user part IDs'), and adds a secondary output (optional names). It is clear what the tool returns, though it never differentiates itself from the closest sibling, measure_parts.

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 prerequisites, and no mention of alternatives such as measure_parts for the same domain. The agent must infer the usage context entirely.

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