Allotrope MCP Server

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list_asms

Lists all available Allotrope Simple Models (ASMs) with their identifiers and descriptions from the bundled reference file. Find available ASM formats for laboratory instruments.

Instructions

List all available Allotrope Simple Models (ASMs).

Retrieves ASM identifiers and descriptions from the local
model_reference.json file bundled with the package.

Returns:
    JSON string with ASM IDs mapped to descriptions, or an
    error key with a description on failure.

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_asms"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 2, 2026

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations, the description carries the full disclosure burden. It reveals that data comes from a local bundled model_reference.json, describes the return format as a JSON string mapping IDs to descriptions, and notes the error-key fallback. This provides a solid behavioral profile for a simple read-only list operation, though it does not explicitly state non-mutating behavior (implied by 'list').

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

Conciseness5/5

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

The description is compact and front-loaded: a one-sentence summary, a single clarifying sentence about the data source, and a concise Returns block. Every sentence adds value, with no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a parameterless listing tool, the description is complete: it states what is listed, where the data comes from, and what the output looks like including error behavior. The presence of an output schema is not an issue because the description already covers the return value, and no additional context is needed.

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

Parameters4/5

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

The tool has zero parameters, so schema coverage is effectively 100%. The description has no parameter details to add, and the baseline of 4 applies because there is nothing to compensate for. No ambiguity exists.

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 clear, specific verb+resource: 'List all available Allotrope Simple Models (ASMs).' It further clarifies scope by mentioning it retrieves identifiers and descriptions from a local file, and the sibling tool names (validate, fetch, describe) contrast with this listing function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for enumerating available ASMs and their metadata, but it does not explicitly state when to use this over sibling tools like fetch_asm_document or describe_asm, nor does it mention any exclusions or alternatives. The usage context is clear from the purpose, but explicit guidance is missing.

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

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