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bbruhn91

Aedifion MCP Server

by bbruhn91

create_plot_view

Generate visual plots for building performance data analysis by configuring project-specific charts with JSON settings.

Instructions

Create a new plot view.

Args: project_id: The project's numeric ID. plot_config: JSON string with the plot configuration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
plot_configYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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 states this is a creation operation, implying mutation, but doesn't cover permissions needed, whether it's idempotent, rate limits, or what happens on success/failure. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

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

Conciseness4/5

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

The description is brief and front-loaded with the main purpose. The two-sentence structure with an 'Args:' section is efficient, though the parameter explanations are minimal. There's no wasted text, but it could be more informative without losing conciseness.

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

Completeness3/5

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

Given that there's an output schema (which handles return values), no annotations, and low schema coverage, the description is minimally adequate. It covers the basic purpose and parameters but lacks behavioral details, usage context, and deeper parameter semantics. For a mutation tool, this leaves gaps in completeness.

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 0%, so the schema provides no parameter descriptions. The description adds minimal semantics: it clarifies that 'project_id' is numeric and 'plot_config' is a JSON string, but doesn't explain what a 'plot configuration' entails, valid formats, or examples. This partially compensates but leaves key details unclear.

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 action ('Create a new plot view') and identifies the resource ('plot view'), which is specific and unambiguous. However, it doesn't differentiate this tool from sibling tools like 'create_analytics_instance' or 'create_controls_instance' that also create resources, nor does it explain what a 'plot view' is in this context.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing an existing project), exclusions, or related tools like 'get_plot_views' or 'delete_plot_view' from the sibling list. It's a basic statement of function without context.

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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