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ZidingWang

Dual Water Tank MCP

by ZidingWang

Fit dual water tank model

fit_dual_water_tank

Fit a dual water tank model to lithium-ion battery charge/discharge data for aging mechanism identification, with automatic parameter bounds and direction inference.

Instructions

Fit one file/sheet with the dual water tank model. Direction, signs, and Cp/Cn bounds can be inferred automatically.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
currentNoCurrent magnitude in A. If omitted, parse C-rate from filename or use 0.
r_upperNo
data_fileYes
directionNoauto
anode_pathNoAnode half-cell OCV file. Defaults to test_cases/25C_0.1C/anode.txt.
max_pointsNo
output_dirNo
sheet_nameNo
generationsNo
voltage_colNo1-based column number or Excel letter.
capacity_colNo1-based column number or Excel letter.
cathode_pathNoCathode half-cell OCV file. Defaults to test_cases/25C_0.1C/cathode.txt.
lower_boundsNo
save_outputsNo
upper_boundsNo
penalty_weightNo
capacity_boundsNo
population_sizeNo
resistance_signNoHow to choose the I*R sign. Auto fits both +I*R and -I*R and keeps the lower full-data RMSE.auto
capacity_lower_factorNo
capacity_upper_factorNo
Behavior2/5

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It mentions automatic inference but does not describe key behaviors such as whether the tool is read-only, what outputs it produces, potential side effects (e.g., file creation), or performance considerations like long runtimes. This is insufficient for a tool with 22 parameters.

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?

Two sentences, no wasted words, front-loaded with the primary action. Excellent conciseness.

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 (22 parameters, no output schema, no annotations), the description is severely incomplete. It does not specify the return value or output format, lack of guidance on required vs optional parameters, and no mention of error conditions or constraints. An agent cannot reliably use this tool without additional context.

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

Parameters2/5

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

Schema description coverage is only 27%, and the description does not explain any parameters beyond mentioning automatic inference of direction, signs, and bounds. It adds little value beyond the schema, which itself leaves many parameters (e.g., seed, r_upper, max_points, generations, etc.) without documentation. The description should clarify key parameters and their roles.

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 it fits the dual water tank model to one file/sheet, distinguishing it from sibling tools like batch_fit (batch) and preview_dual_water_tank_data (preview). It also mentions automatic inference of direction, signs, and bounds, adding specificity.

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 explicit guidance on when to use this tool versus alternatives (e.g., batch fitting or preview). It implies use for single file/sheet fitting but lacks context on prerequisites or scenarios where other tools are preferable.

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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