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stephaneberle9

vegalite-viewer

by stephaneberle9
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Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
VEGALITE_VIEWER_DEBUGNoEnable detailed debug logging. Set to '1' to enable.

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
extensions
{
  "io.modelcontextprotocol/ui": {}
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
upload_dataA

A tool to upload and register a JSON dataset by name for use in subsequent visualizations. When to use this tool:

  • When the user asks to visualize data, call this tool first to register the dataset, then call visualize_data. How to use this tool:

  • Provide a short, descriptive name and the dataset as a list of JSON objects (records).

  • Each object should share a consistent set of keys (fields).

visualize_dataA

A tool to render a Vega-Lite visualization of a registered dataset directly in the chat. When to use this tool:

  • When the dataset is complex or multi-dimensional and a visual representation would be more informative than text.

  • Not useful for single data points. How to use this tool:

  1. Upload the dataset first using the upload_data tool, then reference it by name here.

  2. Analyze the dataset's structure and fields to determine which fields map to which visual channels (x, y, color, size, etc.).

  3. Construct a Vega-Lite specification for the desired chart type. Consult the Vega-Lite documentation (https://vega.github.io/vega-lite/docs) and example gallery (https://vega.github.io/vega-lite/examples) for guidance.

  4. Ensure the spec is a valid instance of the Vega-Lite schema: https://vega.github.io/schema/vega-lite/v6.json. Do not include a "data" key — it is injected automatically from the registered dataset.

Prompts

Interactive templates invoked by user choice

NameDescription
Create a simple chart for a JSON datasetAdvises your LLM create a simple chart with a desired type (e.g., 'bar', 'line', 'pie', etc.) for a provided JSON dataset

Resources

Contextual data attached and managed by the client

NameDescription
vegalite_viewer_appVega-Lite visualization viewer app.
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.5/5.0

Scored across 2 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: upload_data registers datasets, while visualize_data renders them. The descriptions explicitly define when to use each, leaving no ambiguity.

Naming Consistency5/5

Both tools follow the same verb_noun pattern (upload_data, visualize_data), which is consistent and predictable.

Tool Count4/5

At 2 tools, the server is slightly under the typical 3-15 range, but the tools form a complete two-step workflow for the narrow purpose of Vega-Lite visualization, so the count is reasonable.

Completeness4/5

The upload-then-visualize flow covers the core workflow, but there are no management operations (e.g., listing or deleting datasets), which are minor gaps that agents can work around by re-uploading with a new name.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources