Skip to main content
Glama

origin_plot_floating_bar

Create a floating bar plot from table data to visualize the range of values across categories.

Instructions

Create a floating bar plot from table data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
selected_colsNo
graph_nameNo
titleNo
export_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior1/5

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

With no annotations, the description must disclose behavioral traits, but it only says 'create a floating bar plot from table data.' It fails to mention any side effects, required data format, or what happens if the table lacks appropriate columns. This is insufficient for safe invocation.

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

Conciseness2/5

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

The description is extremely concise (one sentence), which is not appropriate for a tool with 5 parameters and no schema descriptions. It lacks structure (e.g., bullet points or sections) and critical details, resulting in underspecification rather than effective 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 (5 parameters, 0% schema coverage, no annotations, an output schema), the description is woefully incomplete. It does not explain what a floating bar plot is, how to select columns, how to name the graph, or what the output contains. This leaves an AI agent with insufficient information.

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

Parameters1/5

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

Schema description coverage is 0%, so the description should compensate by explaining parameters. It only mentions 'from table data', loosely relating to 'path', but does not clarify 'selected_cols', 'graph_name', 'title', or 'export_path'. The description adds virtually no semantic value beyond the parameter names.

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 creates a 'floating bar plot' from 'table data', specifying the exact plot type and data source. This distinguishes it from sibling tools like origin_plot_bar (regular bar) and origin_plot_column (column), which are different chart types.

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 about when to use this tool versus alternatives. Among many plot tools (e.g., bar, column, area), there is no mention of specific conditions or scenarios where a floating bar is appropriate, nor any exclusions.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Ge-Shun/origin-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server