Galaxy Cluster Catalogs MCP Server

crossmatch_catalogs

Cross-match two galaxy-cluster catalogs positionally and by redshift to identify one-to-one counterparts, then write matched pairs, separations, redshift offsets, and mass ratios to CSV.

Instructions

Positionally (+ redshift) cross-match two cluster catalogues; write the matched pairs and mass ratios.

Use for SZ-vs-X-ray (or SZ-vs-SZ) comparisons. Nearest neighbour in A for each B
entry within the radius, made one-to-one (each A keeps its closest B). Typical
radii: 2' for SZ-SZ (ACT/SPT beams ~1'), 3' for Planck (5' beam, larger
positional error) or SZ-vs-eROSITA. Output CSV has *_a / *_b columns,
sep_arcmin, dz and mass_ratio = M500_b / M500_a.

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": {
      "output_dir": "<output_dir>",
      "catalog_a": "<catalog_a>",
      "catalog_b": "<catalog_b>"
    },
    "name": "crossmatch_catalogs"
  }
}

Input Schema

Name	Required	Description
`max_dz`	No	Max \|z_a - z_b\| / (1 + z_a); None = no redshift requirement (entries without z can still match).
`catalog_a`	Yes	Harmonised CSV from fetch_cluster_catalog, or a dataset key.
`catalog_b`	Yes	Harmonised CSV from fetch_cluster_catalog, or a dataset key.
`output_dir`	Yes	Directory for the matched CSV.
`radius_arcmin`	No	Max angular separation in arcmin.

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 supplied, the description carries the full burden and does substantial work: it discloses the matching algorithm (nearest neighbour in A for each B within the radius, then made one-to-one so each A keeps its closest B), that it writes a CSV to disk, and the produced column set including sep_arcmin, dz, and mass_ratio = M500_b / M500_a. It stops short of stating what happens to unmatched entries, whether existing files are overwritten, or failure behaviour.

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 purpose and scope are front-loaded in the first clause, followed by algorithm, usage guidance, and output shape in a logical progression. It is dense but nearly every sentence carries information; the only mild redundancy is restating output columns that the output schema may already cover.

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 five-parameter, mutation-style tool with no annotations, the description is close to sufficient: algorithm, disk write, and output columns are all covered, and an output schema exists for the return structure. The remaining gap is operational edge cases such as overwrite behaviour and handling of unmatched rows in a one-to-one match.

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%, so all five parameters are already documented and the baseline of 3 applies. The description does add real value on radius_arcmin by suggesting typical values tied to instrument beam sizes, but it adds nothing about max_dz or output_dir beyond what the schema already says.

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 opens with a specific verb-plus-resource statement: positionally and in redshift cross-match two cluster catalogues and write matched pairs plus mass ratios. No sibling tool performs cross-matching, and the phrasing makes the operation unmistakable without opening the schema.

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?

It states the intended science case (SZ-vs-X-ray or SZ-vs-SZ comparisons) and gives concrete guidance on which radius to pick for SZ-SZ, Planck, and eROSITA comparisons. What it lacks is any explicit 'do not use this when...' or a named alternative for pre-matched data, but with no overlapping sibling those exclusions are less critical.

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