hex-dashboard-mcp

inject_html_component

Inject styled HTML components like KPI rows, data tables, and alert banners into Hex cells to build dashboards without Python.

Instructions

Write a styled HTML component into a Hex HTML cell. These render in the published app with no Python needed.

component_type: kpi_row — row of metric cards (props: metrics=[{label, value, delta, delta_type}]) section_header — section title + subtitle (props: title, subtitle) data_table — styled HTML table (props: dataframe_var, max_rows) alert_banner — info/warning/error banner (props: message, level) divider — styled horizontal rule (props: label)

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>",
      "component_type": "<component_type>",
      "props": "<props>"
    },
    "name": "inject_html_component"
  }
}

Input Schema

Name	Required	Description	Default
`props`	Yes
`cell_id`	Yes
`auto_run`	No
`project_id`	Yes
`component_type`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 2, 2026

TDQS

A4.2/5.0

Behavior3/5

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

With no annotations, the description carries the transparency burden. It discloses that components render in the published app and require no Python, and it details the component types and their prop structures. It does not mention side effects like overwriting existing cell content, permissions needed, or the impact of the auto_run parameter, leaving some behavioral ambiguity.

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 two sentences of prose followed by a compact, scannable list of component types and their props. Every line adds essential information, and the list structure makes it easy to parse. There is minimal redundancy and no fluff.

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 tool has multiple required parameters and nested props, but the description thoroughly covers the two most complex parameters (component_type and props). An output schema exists, so return-value explanation is not needed. Missing details like auto_run behavior and the prerequisite of an existing HTML cell are minor given the sibling tool context and available schema.

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 has zero description coverage, so the description must compensate. It does so effectively for component_type by listing all five values and their expected props, and it enriches props with example structures like metrics=[{label, value, delta, delta_type}]. It does not explain project_id or auto_run, but those are fairly self-evident from their names and the tool's context.

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 action ('Write') and the target resource ('a styled HTML component into a Hex HTML cell'). It then enumerates concrete component types, which distinguishes it from sibling injection tools like inject_plotly_chart and inject_pydeck_map. The phrasing is specific and non-tautological.

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

Usage Guidelines4/5

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

The description provides clear context: it is for HTML components that render in the published app without Python, and the component type list gives practical usage scenarios. However, it does not explicitly state when to choose this over the plotly or pydeck injection siblings, nor mention any exclusions. The context is clear but there is no explicit alternative comparison.

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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