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davidesg

ART MCP Server

by davidesg

batch_build

Builds one model per time series from multiple input paths, saves .inp files and HTML diagnosis reports to an output directory, and returns a summary table with individual diagnosis figures.

Instructions

Autonomous pipeline for multiple series. Builds one model per series.

Calls build_model for each inp_path, saves individual .inp files and HTML diagnosis reports in output_dir. Returns a summary table and individual diagnosis figures.

Parameters

inp_paths : list of source .inp paths output_dir : directory where output .inp files and HTML reports are saved max_rounds : maximum intervention rounds per series (default 5) run_meg : run MEG test (slow; default False)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
run_megNo
inp_pathsYes
max_roundsNo
output_dirYes
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that the tool saves individual .inp files and HTML diagnosis reports, returns a summary table and figures, and that run_meg is slow. It also notes that it is an 'Autonomous pipeline'. It does not cover edge cases like overwriting behavior or error handling, but for a batch wrapper, this is adequate.

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 front-loaded with a one-sentence purpose summary, followed by a compact parameter list. There is no redundant information, and every sentence adds value. It is appropriately sized for the tool's complexity.

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?

The tool has no output schema, so the description must explain return values, which it does: 'Returns a summary table and individual diagnosis figures.' It also covers the pipeline behavior, file outputs, and parameters. It lacks details on error handling or failure modes, but for a batch tool with 4 well-explained parameters, it is complete enough for invocation.

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?

The schema has 0% description coverage, but the description provides clear semantics for all four parameters: inp_paths as source .inp paths, output_dir as the save directory, max_rounds with a default of 5, and run_meg with a default of False and a note that it is slow. This fully compensates for the missing schema descriptions.

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 tool's function: 'Autonomous pipeline for multiple series. Builds one model per series.' It distinguishes itself from the sibling tool build_model by explicitly saying it 'Calls build_model for each inp_path', making it evident this is the batch counterpart.

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?

Usage context is clear: this tool is for processing multiple series, as implied by 'Autonomous pipeline for multiple series' and 'Calls build_model for each inp_path'. It references the alternative build_model for single-series use, but doesn't explicitly state 'use this when you have multiple series' or provide exclusion criteria. Still, the guidance is strong enough for an agent to infer when to choose it.

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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