Skip to main content
Glama

origin_recommend_chart

Analyze a data file's structure and column meanings to suggest appropriate chart types, optionally guided by a natural-language description of the desired visualization.

Instructions

Recommend chart types from table shape, column semantics, and optional intent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesAbsolute path to a CSV, TSV, TXT, DAT, XLS, or XLSX file.
nrowsNoMaximum number of data rows to read.
x_colNoColumn name or zero-based index to use as X. Defaults to the first column.
z_colNoOptional Z column for contour/XYZ plots.
headerNoZero-based row number to use as column names.
intentNoOptional natural-language chart intent used to guide automatic routing.
y_colsNoColumn names or zero-based indexes to plot as Y. Defaults to all non-X columns.
encodingNoOptional text file encoding.
skiprowsNoRows to skip while reading.
delimiterNoDelimiter for text files. If omitted, CSV/TSV defaults or auto-detection are used.
na_valuesNoAdditional missing value markers.
excel_sheetNoExcel sheet name or zero-based index. Ignored for text files.
x_error_colNoOptional X error column.
y_error_colNoOptional Y error column.
max_recommendationsNoMaximum number of ranked chart recommendations to return.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations provided, the description carries full burden but does not disclose behavioral traits like what happens when data cannot be read, how recommendations are ranked, or error handling. It only gives a high-level overview.

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 a single, focused sentence that front-loads the core action. It could be slightly more informative without sacrificing conciseness.

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

Completeness2/5

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

Given the tool has 15 parameters, no annotations, and an output schema, the description is too minimal. It does not explain the recommendation logic, output format, or how to interpret results, leaving the agent under-informed.

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 baseline is 3. The description hints at some parameters (table shape, column semantics, intent) but adds little beyond the schema's parameter names and types.

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 recommends chart types based on table shape, column semantics, and optional intent. It uses a specific verb ('recommend') and resource ('chart types'), differentiating from sibling tools like origin_plot which actually plot data.

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 guidance on when to use this tool versus alternatives such as origin_plot_auto or origin_plan_figure_spec. It does not mention prerequisites, exclusions, or typical use cases.

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