Plant Simulation Research Bridge

Overview Schema Source Related Servers Discussions Admin

model_read_counters

Reads StatNumIn, StatNumOut, and StatWorkingTime for named objects in a frame to support scheduling analysis and model verification.

Instructions

Read StatNumIn, StatNumOut and StatWorkingTime for named objects in a frame.

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": {
      "objects": "<objects>"
    },
    "name": "model_read_counters"
  }
}

Input Schema

Name	Required	Description	Default
`objects`	Yes
`work_dir`	No
`frame_path`	No
`model_root`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 11, 2026

TDQS

C2.7/5.0

Behavior2/5

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

With no annotations, the description carries the full behavioral burden, and it only implies read-only via the verb "Read". It says nothing about what happens if an object name is unknown, whether the frame must be active, permission needs, or what the response contains.

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 efficient sentence with the output statistics front-loaded and no filler. It is appropriately sized, but the density of unexplained domain jargon keeps it from being maximally clear.

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?

For a four-parameter tool with no annotations, no output schema, and zero schema descriptions, the definition is far too thin. It neither explains the ambiguous path parameters nor describes the shape of the returned counter data, so an agent would be guessing on both input and output.

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% across four parameters, yet the description only glosses "objects" and hints at "frame" via frame_path. The work_dir and model_root parameters, which are the most likely sources of invocation error, receive no explanation anywhere.

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?

The description uses a specific verb ("Read") and names the exact three statistics returned (StatNumIn, StatNumOut, StatWorkingTime) plus the scope (named objects in a frame). This distinguishes it from generic siblings like model_get_property, though the domain jargon limits how self-explanatory it is.

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 statement of when to use this tool versus model_get_property or model_object_info, and no prerequisites such as whether the model must be open or a frame loaded. The agent must infer all usage conditions from the single sentence.

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