hex-dashboard-mcp

inject_plotly_chart

Inject a Plotly chart into a Hex notebook cell with automatic design-system styling. Define chart type, dataframe, and column mappings to generate the chart.

Instructions

Write a fully styled Plotly chart into a Hex Python cell. Design system (dark theme, color palette, typography) is applied automatically.

chart_type: line | bar | scatter | area | histogram | box | heatmap | funnel | treemap dataframe_var: name of the DataFrame variable already defined in the notebook x_col / y_col: column names for axes color_col: optional column for series coloring extra_kwargs: any additional px.chart() keyword args as a string, e.g. 'barmode="group"' auto_run: if True, immediately triggers run_and_wait to push to app view

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "project_id": "<project_id>",
      "cell_id": "<cell_id>",
      "chart_type": "<chart_type>",
      "dataframe_var": "<dataframe_var>",
      "x_col": "<x_col>",
      "y_col": "<y_col>"
    },
    "name": "inject_plotly_chart"
  }
}

Input Schema

Name	Required	Description	Default
`title`	No
`x_col`	Yes
`y_col`	Yes
`height`	No
`cell_id`	Yes
`auto_run`	No
`color_col`	No
`chart_type`	Yes
`project_id`	Yes
`extra_kwargs`	No
`dataframe_var`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 2, 2026

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context such as 'Design system (dark theme, color palette, typography) is applied automatically' and 'auto_run: if True, immediately triggers run_and_wait to push to app view.' However, it does not clarify whether writing the chart overwrites existing cell content or how it handles errors, leaving key behavioral aspects undisclosed.

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 efficient and well-structured. It opens with a clear one-sentence purpose, adds a single sentence about automatic styling, and then presents a concise parameter list in a scannable format. No redundant phrases or filler text are present.

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 the tool's complexity (11 parameters, 0% schema coverage), the description covers the essential parameters and environment. However, it omits critical context about whether the chart code replaces or appends to the cell's existing source, and what happens when the cell is run (beyond the auto_run flag). The presence of an output schema lessens the need to describe return values, so the gaps around cell mutation behavior are the main deficiency.

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

Parameters4/5

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

The schema provides zero description coverage, so the description compensates by explaining the key functional parameters: chart_type (with allowed values), dataframe_var, x_col/y_col, color_col, extra_kwargs (with an example), and auto_run. It does not explain title or height, but those are relatively self-explanatory. The description adds substantial meaning beyond the raw schema for the parameters that matter most.

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 opens with 'Write a fully styled Plotly chart into a Hex Python cell,' which is a specific verb+resource combination. It clearly distinguishes from sibling inject tools like inject_pydeck_map and inject_html_component by explicitly naming Plotly as the chart library and Hex Python cell as the target environment.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage through the list of supported chart types and the note that the design system is applied automatically. However, it does not explicitly state when to use this tool over alternatives like inject_pydeck_map or inject_html_component, nor does it mention any exclusions or prerequisites beyond needing an existing DataFrame variable.

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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