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Algenta MCP Server

by io.github.thyn-ai
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list_distributions

Read-onlyIdempotent

List the probability distribution types supported in simulation variables — normal, uniform, triangular, lognormal, and fixed — each with its required parameters and a ready-to-use example. Read this before writing variable definitions for simulate, score, compare, or submit_job. Read-only.

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_distributions"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description only adds 'Read-only' redundantly. It does add value by revealing the content of the result (required parameters and examples), but does not disclose any deeper behavioral aspects like pagination, rate limits, or auth. With annotations covering the safety profile, a 3 is appropriate.

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?

Two sentences, no wasted words, and the most important information (what it lists) is front-loaded. The usage guidance is directly attached at the end, making it compact and readable.

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 simple list tool with no output schema and no parameters, the description fully covers what the tool returns (distribution types, required parameters, examples) and when to use it. Nothing an agent needs to call or interpret this tool is missing.

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 and schema coverage is 100%, so there is nothing for the description to add about input semantics. The description's mention of 'required parameters' refers to the distribution definitions in the output, not tool inputs. Baseline for 0 params is 4.

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?

Description uses a specific verb ('list') and resource ('probability distribution types supported in simulation variables'), enumerates the exact types, and implicitly distinguishes itself from sibling list tools by scoping to simulation variable distributions. An agent can immediately tell what this tool does.

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?

Explicitly tells the agent to read this before writing variable definitions for simulate, score, compare, or submit_job, which is clear when-to-use guidance. It does not mention when not to use or name alternative tools, but for a reference/listing tool this is sufficient and better than vague.

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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