Skip to main content
Glama
ghostiee-11

holoviz-viz-mcp

by ghostiee-11

load_data

Load data into the server from CSV or JSON strings, or from a URL supporting CSV, JSON, Parquet, and Excel formats.

Instructions

Load data into the server from CSV text, JSON text, or a URL.

Supports CSV, JSON, Parquet, and Excel formats. For URL loading, the format is auto-detected from the file extension.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoURL to fetch data from (supports csv, json, parquet, xlsx)
nameNoOptional name for the dataset (auto-generated if not provided)
formatNoData format when using csv_data/json_data — 'csv' or 'json'csv
csv_dataNoCSV-formatted string data
json_dataNoJSON-formatted string data (records or columnar orientation)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries full burden for behavioral transparency. It discloses auto-detection for URL load but does not mention what happens on overwrite or naming conflicts, error handling, or dataset naming limits.

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?

Two sentences with no fluff, front-loaded with the core purpose. Every sentence earns its place.

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?

Given the presence of an output schema, the description adequately explains the tool's purpose and supported formats. It could be more complete by mentioning behavior on duplicate names or URL failures, but overall it is sufficient.

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?

Schema coverage is 100%, so baseline is 3. The description adds value by summarizing data sources (CSV text, JSON text, URL) and noting auto-detection for URL formats, which is extra information beyond the schema.

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 loads data from CSV text, JSON text, or a URL, specifying the verb 'load' and resource 'data'. It distinguishes from sibling tools like load_sample_data by focusing on external data import.

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 mentions supported formats and auto-detection for URLs, providing clear context on data sources. However, it lacks explicit guidance on when to use this tool versus alternatives like load_sample_data or generate_large_dataset, and no exclusion criteria are given.

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/ghostiee-11/holoviz-viz-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server