LS-PrePost-MCP

by lwz20210407

Overview Schema Source Related Servers Discussions Admin

probe_scl

Count nodes in a keyword model using native SCL, avoiding embedded Python. Enables direct LS-PrePost node-count queries for preprocessing verification.

Instructions

Count nodes in a keyword model through native SCL without requiring embedded Python.

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": "probe_scl"
  }
}

Input Schema

Name	Required	Description	Default
`model`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 30, 2026

TDQS

C2.4/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It notes the native SCL implementation runs without embedded Python, which is minor execution context, but omits whether it is read-only, what it returns, or how the model is resolved.

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 padding. It is efficient, though the 'without requiring embedded Python' clause is somewhat tangential to the core action.

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?

With no annotations, no output schema, and an undocumented single parameter, the description is too thin. It never explains what the returned count represents or how the model is supplied, leaving real gaps for the agent.

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?

The single 'model' parameter has 0% schema description coverage, so the description must compensate. It implies the argument is a 'keyword model' but does not clarify whether it is a file path, name, or identifier, adding only marginal value over the raw schema.

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

Purpose3/5

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

The description names a verb and resource (count nodes in a keyword model) which is reasonably clear, but the tool name 'probe_scl' implies SCL exploration rather than node counting, creating ambiguity. It also fails to distinguish itself from siblings like list_nodes or inspect_model, leaving the agent unsure which node-related tool to pick.

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?

There is no explicit guidance on when to use this tool versus alternatives. The phrase 'without requiring embedded Python' hints at a condition for preferring it, but no when-to-use, when-not, or sibling alternatives are stated.

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