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markomitranic

Data Visualization MCP Server

by markomitranic
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

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

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
save_dataB

A tool which allows you to save data to a named table for later use in visualizations. When to use this tool:

  • Use this tool when you have data that you want to visualize later. How to use this tool:

  • Provide the name of the table to save the data to (for later reference) and the data itself.

visualize_dataA

A tool which allows you to produce a data visualization using the Vega-Lite grammar. When to use this tool:

  • At times, it will be advantageous to provide the user with a visual representation of some data, rather than just a textual representation.

  • This tool is particularly useful when the data is complex or has many dimensions, making it difficult to understand in a tabular format. It is not useful for singular data points. How to use this tool:

  • Prior to visualization, data must be saved to a named table using the save_data tool.

  • After saving the data, use this tool to visualize the data by providing the name of the table with the saved data and a Vega-Lite specification.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

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

A3.6/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have completely distinct purposes with no overlap. 'save_data' is for storing data in a table, while 'visualize_data' is for creating visualizations from saved data. The descriptions clearly differentiate their functions and usage contexts.

Naming Consistency5/5

Both tools follow a consistent verb_noun naming pattern ('save_data' and 'visualize_data'). The naming style is uniform throughout, using snake_case with clear action-object pairs that accurately reflect their functions.

Tool Count2/5

With only 2 tools, this server feels severely under-scoped for a data visualization domain. A complete visualization workflow would typically require tools for data manipulation, chart type selection, configuration adjustments, or exporting visualizations. The current set is too minimal for effective agent use.

Completeness2/5

The tool surface has significant gaps for a data visualization server. There are no tools for data transformation, filtering, or aggregation before visualization. Missing are tools for different visualization types, chart customization, or exporting results. The dependency on Vega-Lite specifications without helper tools creates a steep learning curve for agents.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources