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lichman0405

raspa-mcp

by lichman0405

plot_isotherm_comparison

Generate a combined isotherm plot from multiple MOF datasets for direct comparison. Input labeled isotherm data from top candidates to produce a single publication-ready PNG.

Instructions

Generate a multi-MOF comparison isotherm plot (PNG) — all MOFs on one figure.

datasets is a list of dicts, each representing one MOF: { "label": "MIL-101(Cr)", # legend label "isotherm_data": [{...}, {...}, ...] # same format as plot_isotherm() }

Designed for the final "top-N candidates" comparison step: pass in the isotherm_data from each MOF's parse_raspa_output() call together with a descriptive label, and receive a single publication-ready comparison figure.

pressure_unit / loading_key: same semantics as plot_isotherm(). output_path: absolute path where the PNG will be saved.

Returns: {"status": "ok", "path": ..., "n_series": ...} or {"status": "error", "message": ...}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetsYes
moleculeNo
loading_keyNoloading_mol_kg
output_pathYes
pressure_unitNoPa
temperature_KNo
Behavior4/5

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

No annotations are present, so the description bears full responsibility for transparency. It discloses the output format (PNG), the return structure including success and error cases, and that it writes to an absolute path. It does not mention file overwrite behavior or permission requirements, but for a plotting tool the disclosure is reasonable.

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 well-structured and concise: a one-sentence summary, a code block for the complex parameter, and terse notes for the remaining parameters and return value. Every sentence contributes meaningful information without redundancy.

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?

Given the tool has 6 parameters and no output schema, the description provides the essential invocation details, return format, and workflow context. It covers the most complex parameter (datasets) and returns status/path/n_series. It leaves out explanation of two optional parameters, but this is acceptable for a plotting utility.

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?

With 0% schema description coverage, the description compensates by thoroughly explaining the 'datasets' parameter with a concrete example, and notes that pressure_unit/loading_key follow the same semantics as plot_isotherm(). However, it omits explanation of the molecule and temperature_K parameters, and only gives a minimal description for output_path as 'absolute path'.

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 explicitly states 'multi-MOF comparison isotherm plot (PNG)' and 'all MOFs on one figure', clearly identifying the tool's function. It also distinguishes it from the sibling plot_isotherm by emphasizing multi-MOF comparison, and mentions its role in the 'top-N candidates' workflow.

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

Usage Guidelines4/5

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

The description provides clear usage context: 'Designed for the final top-N candidates comparison step' and instructs to pass isotherm_data from parse_raspa_output(). However, it does not explicitly contrast with plot_isotherm (single-MOF) or state when not to use this tool, though the multi-MOF vs single-MOF distinction is implied.

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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