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Coolprop

by io.github.pipeworx-io
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Coolprop List Fluids

coolprop_list_fluids
Read-onlyIdempotent

The exact canonical fluid names this CoolProp build recognizes for coolprop_fluid_properties (~124 fluids: water, common refrigerants, hydrocarbons, cryogens, siloxanes, ...). No aliases — e.g. "CarbonDioxide", not "CO2"; use this list rather than guessing a short name.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

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

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
countNo
fluidsNo
sourceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the safety hints (readOnlyHint: true), the description provides crucial behavioral context about the data itself. By stating 'No aliases — e.g. CarbonDioxide, not CO2,' it establishes a strict behavioral constraint on the expected output format, which is vital for downstream agents.

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

Conciseness5/5

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

The description is concise, packed with high-value constraints, and uses an effective real-world example (CO2 vs CarbonDioxide) to clarify a critical point instantly. There is zero waste in the text.

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

Completeness5/5

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

As a static reference list with no input parameters, the description is maximally complete. It defines the scope, the exact data fidelity, and the required naming conventions for the agent to use the output correctly.

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

Parameters4/5

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

Although the schema has 0 parameters, the description adds massive semantic value by defining the *nature* of the data in the output (canonical names, ~124 fluids). This defines the content and structure of the results far beyond what an empty schema can convey, justifying a score above baseline.

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

Purpose5/5

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

The purpose is extremely clear: listing the exact canonical fluid names recognized by the specific CoolProp build. It clearly differentiates itself from the sibling tool, coolprop_fluid_properties, by instructing the user to use this list for validation rather than using it directly for property retrieval.

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

Usage Guidelines5/5

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

The usage is guided by necessity. It tells the user to use this tool 'rather than guessing a short name,' providing explicit instructions on when this tool is required (i.e., when the user lacks the correct canonical identifier).

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

All Coolprop toolsInspect
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