Supernova Cosmology MCP Server

fetch_sn_salt2_params

Fetch per-supernova SALT2/SALT3 light-curve fit parameters (x1, c, mB, errors, covariances, host mass, bias corrections) for Tripp standardization.

Instructions

Fetch per-SN SALT2/SALT3 light-curve fit parameters: x1, c, mB and their errors and covariances, HOST_LOGMASS and bias corrections.

Use this to get the per-SN catalog data needed for Tripp standardisation via
standardize_sn_salt2. Pantheon+ uses SALT2 fits (Brout+22); DES-SN5YR uses SALT3 (Kenworthy+21).
The output CSV has one row per SN and includes the released distance modulus for comparison.

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": "fetch_sn_salt2_params"
  }
}

Input Schema

Name	Required	Description	Default
`zmax`	No	Keep z <= zmax.
`zmin`	No	Keep z > zmin.
`dataset`	No	pantheonplus or des5yr.	pantheonplus
`output_dir`	Yes	Directory for the CSV.

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

A3.9/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses useful behavior: output is a CSV with one row per SN and includes the released distance modulus, and dataset choice determines SALT2 vs SALT3. However, it says nothing about whether files are overwritten in output_dir, whether the fetch requires network access or cached data, or expected size/runtime for a retrieval tool.

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?

Three short sentences, front-loaded with the returned fields followed by usage and dataset notes. Little waste, though the dataset-provenance sentence could be tightened and the trailing blank line suggests mild padding.

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?

With an output schema present, the description needn't detail return columns, and the fully documented 4-parameter schema is covered. Provenance and downstream-consumer context make it adequate for a data-fetch tool; only file-side-effect and access behavior are missing.

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% and the schema already documents zmin/zmax semantics, the dataset enum, and output_dir. The description adds no parameter-level detail beyond what the schema states, so the baseline 3 applies.

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 names a specific verb ('Fetch') and a precise resource ('per-SN SALT2/SALT3 light-curve fit parameters'), then enumerates the returned quantities (x1, c, mB, errors, covariances, HOST_LOGMASS, bias corrections). This clearly distinguishes it from siblings like fetch_sn_lightcurve (photometry) and standardize_sn_salt2 (which consumes this data).

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 downstream use ('needed for Tripp standardisation via standardize_sn_salt2') and clarifies dataset provenance (Pantheon+ = SALT2, DES-SN5YR = SALT3), which helps an agent pick the right dataset value. It does not explicitly state when not to use it or contrast it with fetch_sn_sample, which is the main remaining gap.

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