PyMemSim-MCP

simulate_gas_hfm

Simulate gas separation in hollow-fiber membrane modules using thermodynamic reference data. Define components, feed conditions, membrane properties, and flow patterns to analyze permeation.

Instructions

run a gas hollow-fiber membrane simulation, using pyThermoDB YAML reference content to build the model source.

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": {
      "request": "<request>"
    },
    "name": "simulate_gas_hfm"
  }
}

Input Schema

Name	Required	Description	Default
`request`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Jul 12, 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 burden of behavioral disclosure. It only states 'run a simulation' without explaining what the simulation does (e.g., steady-state, transient), what inputs are required beyond the YAML, or any side effects. This is insufficient for a complex tool.

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

Conciseness3/5

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

The description is a single sentence, which is concise, but it is under-specified and lacks structure. It does not provide enough information to be useful, so conciseness does not compensate for missing content.

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

Completeness1/5

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

Given the tool's complexity (many parameters, no annotations, output schema exists), the description is far too minimal. It fails to cover essential aspects like simulation behavior, parameter roles, or how to structure inputs. The description is completely inadequate.

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

Parameters1/5

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

Schema description coverage is 0% according to context, meaning no parameter descriptions in the schema. The description adds no parameter information at all, only mentioning 'using pyThermoDB YAML reference content'. This leaves the many parameters completely unexplained.

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 states the tool runs a gas hollow-fiber membrane simulation and mentions using pyThermoDB YAML reference content. However, it does not distinguish itself from sibling tools like check_yaml_reference or hfm_feed_flow_rate_analyzer, lacking differentiation.

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?

The description provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites such as having a valid YAML reference. This omission makes it hard for an agent to decide when to invoke this tool.

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

Deploy Server