odsbox-jaquel-mcp

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plot_comparison_notebook

Generate a Jupyter notebook to compare ASAM ODS measurements by plotting specified quantities as scatter, line, or subplots, using filter conditions and the active ODS connection.

Instructions

Generate a Jupyter notebook for comparing measurements.

Uses the active ODS connection (established via ods_connect or ods_connect_using_env). The generated notebook reads the password from the ODS_PASSWORD environment variable at runtime so no credentials are embedded in the notebook file.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`title`	No	Notebook title	Measurement Comparison
`plot_type`	No	Type of plot ("scatter", "line", or "subplots")	scatter
`output_path`	No	Optional path to save notebook (.ipynb file)
`available_quantities`	No	List of all available quantities (for documentation)
`measurement_quantity_names`	Yes	Names of quantities to plot
`measurement_query_conditions`	Yes	Filter conditions for measurements (MeaResult attributes). Example: {"Name": {"$like": "Profile_*"}} or {"TestStep.Test.Name": {"$eq": "MyTest"}}

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 7, 2026

TDQS

A3.8/5.0

Behavior4/5

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

The description discloses an important behavioral detail: the generated notebook reads the password from ODS_PASSWORD at runtime, avoiding embedded credentials. This goes beyond the annotations and adds useful security-relevant context. No contradiction with readOnlyHint=false or destructiveHint=false.

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 three short sentences with no redundant content. The main purpose is front-loaded, and the connection and credential-handling details each earn their place.

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 description covers the key prerequisite connection requirement and the security-relevant caching behavior. Combined with the rich input schema, enum options, and output schema, an agent has enough context to invoke the tool, though explicit sibling differentiation is left unstated.

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?

Schema description coverage is 100%, so the input schema already documents all parameters including examples for measurement_query_conditions. The description does not add additional parameter-level meaning, making the baseline 3 appropriate.

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 verb-action 'Generate a Jupyter notebook' and the resource purpose 'comparing measurements'. It differentiates itself from the sibling plot_generate_code by specifying the notebook format rather than generic code generation.

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 mentions a prerequisite (active ODS connection via ods_connect or ods_connect_using_env) but gives no guidance on when to choose this tool over alternatives such as plot_generate_code. There are no exclusions or explicit 'when to use' instructions.

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