Galaxy Cluster Catalogs MCP Server

list_clusters_datasets

Lists available public galaxy-cluster datasets by product type—catalogs, summary statistics, and published results—so users can choose the right data and tools for analysis.

Instructions

List the public datasets this server exposes. Call this first.

Products are grouped as catalogs (object-level data), summary statistics (measurements with covariance) and published results (chains / parameter constraints, for comparison only). Build analyses on catalogs and summary statistics. Each entry also gives access (bundled vs downloaded on first use) and which tools read it.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_clusters_datasets"
  }
}

Input Schema

Name	Required	Description	Default
`kind`	No	Only datasets offering this kind of product: 'catalog', 'summary_statistic' or 'published_result'. null = all.

Output Schema

Name	Required	Description	Default
`files`	Yes
`status`	Yes
`message`	Yes
`metadata`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 6, 2026

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations, the description carries the full load and does disclose non-obvious behavior: entries indicate access mode (bundled vs downloaded on first use) and which tools read each dataset. It omits any statement about side effects or latency of first-use downloads, keeping it below a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The directive 'Call this first' is front-loaded and each sentence carries information. The middle paragraph is denser than needed, packing taxonomy definitions into one long clause, but nothing is truly redundant.

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?

An output schema exists, so return-value explanation is not required, and the description still usefully summarizes what each entry contains (access mode, consuming tools). For a low-complexity list tool this is nearly complete, missing only how to drill into a specific dataset.

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

Parameters3/5

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

Schema coverage is 100% and the single 'kind' parameter is fully documented in the schema. The description's product taxonomy (catalog, summary statistics, published results) mirrors the enum values conceptually but never names the parameter or its filtering behavior, so it adds little 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?

States a specific verb+resource ('List the public datasets this server exposes') and additionally frames the tool's role with 'Call this first,' which separates it from discoverable siblings like describe_clusters_dataset and fetch_cluster_catalog.

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?

Gives explicit sequencing ('Call this first') and downstream guidance (build analyses on catalogs and summary statistics; published results are for comparison only). It stops short of naming alternatives for per-dataset detail, but the workflow orientation is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.