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control-expert-mcp

control-expert-mcp

by apexsotjo-blip
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get_master_dtm_dataset

Retrieve Modbus TCP scan lines from the master DTM dataset XML, providing slave device configuration details such as connection numbers, read/write addresses, and object IDs.

Instructions

Read the master/CPU DTM dataset XML — THE document holding the Modbus TCP scan lines (M580 DTMs XML Dataset spec).

Each scanned device is a under ; its holds one per scan line with Parameters attributes requestSettingBit (connection number), requestSettingRDAddress/RDLength, requestSettingWRAddress/WRLength, requestInputObjID/requestOutputObjID. dtm defaults to the first (CPU) DTM node.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_master_dtm_dataset"
  }
}

Input Schema

NameRequiredDescriptionDefault
dtmNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral burden. It reveals that dtm defaults to the first CPU node and describes the XML structure in detail, showing this is a read operation on the master dataset. It does not cover error behavior or side effects, but for a read-only tool the essential behavior is transparent.

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?

The description is front-loaded with the primary purpose and then provides relevant structural detail about the XML document. The XML breakdown is dense but useful given the lack of an output schema. It is not overly verbose and every section serves a purpose.

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?

The description covers the key context: what the dataset represents, how it is structured, and the default behavior of the only parameter. It lacks details about error cases and valid dtm values, but for a read tool with a single optional parameter, the provided context is largely sufficient.

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?

The input schema provides only a bare optional string with default '', and schema_description_coverage is 0%. The description adds meaning by associating dtm with the DTM node and explaining the default CPU behavior, but it does not specify how to reference non-default DTMs or what formats are accepted. This partial compensation keeps it above baseline but leaves a gap.

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 clearly identifies the action ('Read') and the specific resource ('master/CPU DTM dataset XML'), and elaborates on its content (Modbus TCP scan lines). It distinguishes itself from generic DTM reads by emphasizing the master/CPU scope, though it does not explicitly name conflicting siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies its usage by stating what it reads and noting the default DTM node, but it does not provide explicit when-to-use versus alternative tools like get_dtm_dataset or set_master_dtm_dataset. The context is clear for the master dataset case, but no exclusions or alternative routing is given.

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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