Galaxy Cluster Catalogs MCP Server

describe_catalog_columns

Explains catalog column mappings, units, mass conventions, and native FITS columns so you can compare masses across surveys before analysis.

Instructions

Explain a catalogue's columns: harmonised mapping, units, mass convention, and native FITS columns.

Use before comparing masses across surveys - each survey's M500 comes from
a different scaling relation, and ``snr`` means different things.

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": {
      "dataset": "<dataset>"
    },
    "name": "describe_catalog_columns"
  }
}

Input Schema

Name	Required	Description	Default
`dataset`	Yes	Catalogue key: psz2, spt_sz, sptpol_100d, spt_ecs, spt3g_5yr, act_dr6, erass1, erass1_cosmo (see list_clusters_datasets).
`show_native`	No	Also list every native FITS column with unit and an example.

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

A3.6/5.0

Behavior3/5

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

No annotations are supplied, so the description carries the full behavioral burden. It makes clear this is an informational/metadata lookup with no side effects and tells the agent what content to expect, but says nothing about authentication, cost, or whether the catalogue must exist beforehand.

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?

Two short sentences with the content summary front-loaded and the usage rationale second — nothing is padding. Minor friction from leftover RST double-backtick markup around `snr` in an otherwise plain-text field.

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?

For a two-parameter read-only lookup with a declared output schema and full schema coverage, the description covers what is returned and when it matters. The one real gap is sibling disambiguation against describe_clusters_dataset, which a complete definition should address.

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 description coverage is 100%, so the schema already documents both `dataset` (with its valid keys and a pointer to list_clusters_datasets) and `show_native` (default true, native FITS columns with unit and example). The description's mention of "native FITS columns" loosely echoes `show_native` but adds no format or value detail beyond the schema; baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("Explain a catalogue's columns") and enumerates the substance returned: harmonised mapping, units, mass convention, native FITS columns. It does not, however, distinguish itself from the near-identically named sibling describe_clusters_dataset, leaving the agent to infer the difference.

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 an explicit trigger condition — "Use before comparing masses across surveys" — with a concrete rationale about differing M500 scaling relations and ambiguous `snr`. It lacks any when-not-to-use or a named alternative (e.g. describe_clusters_dataset), which keeps it below a 5.

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