Supernova Cosmology MCP Server

plot_sn_covariance

Plots the statistical plus systematic correlation matrix of a supernova sample, sorted by redshift, to reveal off-diagonal calibration structure and verify that covariance-aware fits are needed.

Instructions

Image of the stat+sys correlation matrix (rows sorted by redshift).

Use to show how strongly SNe are correlated by calibration/systematics (off-diagonal structure is what makes diagonal-error fits wrong) and to sanity-check the matrix.

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>"
    },
    "name": "plot_sn_covariance"
  }
}

Input Schema

Name	Required	Description	Default
`zmin`	No	z cut (None = release standard).
`dataset`	No	SN sample.	pantheonplus
`output_dir`	Yes	Directory for the PNG.

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?

With no annotations, the description carries the full burden. It does disclose that the output is an image file written to a directory and that rows are redshift-sorted, but it says nothing about overwrite behavior, required permissions, runtime cost for large samples, or what happens with an invalid zmin range.

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 artifact described first and the rationale second; nothing is padded or repeated. The scientific aside about off-diagonal structure is justified because it explains what the plot is for.

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 values need not be described, and only three simple parameters are involved. The definition covers what the plot is and why to call it; only the file-writing side effects remain implicit.

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 the three parameters (zmin, dataset, output_dir) are already documented by the schema itself, including the enum and the 'None = release standard' default. The description adds no per-parameter meaning beyond the schema, so the 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?

"Image of the stat+sys correlation matrix (rows sorted by redshift)" states a concrete artifact and its content, which is clearly distinct from sibling plot tools such as plot_sn_contours or plot_hubble_diagram. It stops short of naming those siblings, but the resource is unambiguous.

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 second sentence gives two concrete use cases: showing how strongly SNe are correlated via calibration/systematics, and sanity-checking the matrix. There are no explicit when-not-to-use conditions or named alternatives, but the context is clear enough to route an agent.

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