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gabrielserrao

pyResToolbox MCP Server

gas_water_content

Calculate water content in natural gas to prevent hydrate formation and design dehydration systems based on pressure, temperature, and gas gravity inputs.

Instructions

Calculate water content of natural gas.

CRITICAL GAS PROCESSING TOOL - Computes the amount of water vapor that natural gas can hold at given pressure and temperature conditions. Essential for hydrate prevention, dehydration unit design, and pipeline operation. Water content decreases with increasing pressure and decreasing temperature.

Parameters:

  • sg (float, required): Gas specific gravity (air=1.0). Valid: 0.55-3.0. Typical: 0.6-1.2. Example: 0.7.

  • degf (float, required): Temperature in °F. Valid: -460 to 1000. Typical: 40-200°F. Example: 100.0.

  • p (float or list, required): Pressure(s) in psia. Must be > 0. Can be scalar or array. Example: 1000.0 or [500, 1000, 2000].

Water Content Behavior:

  • Decreases with increasing pressure (less water can dissolve)

  • Decreases with decreasing temperature (less water vapor)

  • Typical range: 5-200 lb/MMSCF at pipeline conditions

  • At high pressure/low temperature: <10 lb/MMSCF

Hydrate Formation: Gas-water systems form solid hydrates (ice-like structures) at certain P-T conditions. Hydrates can block pipelines and equipment. Gas must be dehydrated below:

  • Hydrate formation temperature at operating pressure

  • Typical target: <7 lb/MMSCF for pipeline operation

  • Typical target: <0.1 lb/MMSCF for LNG/cryogenic processes

Correlation: Uses McCain correlation (1990) based on experimental data for sweet natural gas. Valid for typical pipeline and processing conditions.

Applications:

  • Hydrate Prevention: Determine minimum dehydration requirement

  • Dehydration Unit Design: Size glycol contactors and regenerators

  • Pipeline Corrosion: Assess water-related corrosion risk

  • Gas Processing: Design dehydration systems for sales gas

  • Sales Gas Specs: Ensure compliance with water content limits

Returns: Dictionary with:

  • value (float or list): Water content in lb/MMSCF (matches input p shape)

  • method (str): "McCain (1990) correlation"

  • units (str): "lb/MMSCF"

  • inputs (dict): Echo of input parameters

  • note (str): Hydrate prevention guidance

Common Mistakes:

  • Using separator temperature instead of pipeline/processing temperature

  • Pressure in barg/psig instead of psia (must be absolute)

  • Not understanding hydrate formation conditions

  • Confusing water content (lb/MMSCF) with water dew point (°F)

  • Temperature in Celsius instead of Fahrenheit

Example Usage:

{
    "sg": 0.7,
    "degf": 100.0,
    "p": [500, 1000, 2000]
}

Result: Water content decreases from ~50 lb/MMSCF at 500 psia to ~20 lb/MMSCF at 2000 psia.

Note: Water content is critical for pipeline operation. Always check against hydrate formation curve. For hydrate prevention, compare to hydrate formation temperature at operating pressure. Typical pipeline requirement: <7 lb/MMSCF.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description carries the full burden and excels by disclosing behavioral traits: it explains the correlation method (McCain 1990), valid ranges, typical outputs (5-200 lb/MMSCF), critical applications, common mistakes, and return structure. It also details how water content behaves with pressure and temperature changes, providing comprehensive operational context.

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 well-structured with clear sections (Parameters, Water Content Behavior, Applications, Returns, etc.), but it is lengthy. Every sentence adds value, such as explaining hydrate formation and common mistakes, but it could be more front-loaded; the core purpose is stated early, but some details might be condensed for better conciseness without losing essential information.

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?

Given the complexity of the tool (engineering calculation with critical safety implications), no annotations, and an output schema that documents return values, the description is complete. It covers purpose, usage, parameters, behavior, applications, returns, and common pitfalls, providing all necessary context for an AI agent to invoke the tool correctly and understand its significance in gas processing.

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

Parameters5/5

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 fully. It adds extensive meaning beyond the bare schema: defines each parameter (sg, degf, p) with units, valid ranges, typical values, examples, and behavioral effects (e.g., water content decreases with increasing pressure). This thoroughly documents all parameters, making the tool's inputs clear and actionable.

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 description clearly states the specific purpose: 'Calculate water content of natural gas' with the verb 'calculate' and resource 'water content of natural gas'. It distinguishes from siblings by focusing on water content calculation specifically for hydrate prevention and dehydration design, unlike other tools that handle properties like compressibility, density, or viscosity.

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?

Explicit guidance is provided on when to use this tool: 'Essential for hydrate prevention, dehydration unit design, and pipeline operation.' It also specifies alternatives implicitly by mentioning related concepts (e.g., water dew point) and contexts where it's critical, such as pipeline operation vs. LNG processes, helping differentiate from sibling tools that might calculate other gas properties.

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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