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Neuratel-AI

Neuratel MCP Server

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by Neuratel-AI

get_system_variables_catalog

List the platform's auto-injected system__* template variables to validate safe placeholders for agent prompts and distinguish them from user-supplied dynamic variables when building call payloads.

Instructions

List the platform's built-in system__* template variables.

These are auto-injected at call time by the platform — you never pass them in dynamic_variables. Use this catalog to:

  • Validate which {{system__*}} placeholders are safe to embed in agent prompts

  • Distinguish auto-injected variables from user-supplied ones when building dynamic_variables payloads for make_call

Each entry has name, description, and available_on (the channels where the variable resolves to a real value vs falling back to "").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully carries the burden and excels: it discloses that variables are auto-injected at call time, explains the channel fallback behavior, and outlines the entry structure. This goes beyond a simple 'list' and provides valuable context.

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 well-structured with a clear lead sentence, bullet points for use cases, and a concise final sentence about return fields. Every sentence adds value and there is no redundancy.

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?

Given the tool's simplicity (0 parameters, output schema present), the description is complete. It covers purpose, usage, and return format, and it handles lack of annotations by providing thorough context, leaving no critical gaps.

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 the baseline is 4. The description adds relevant context by clarifying that system variables are not passed in dynamic_variables, which indirectly reinforces the no-parameter expectation for this tool.

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 uses a specific verb ('List') and clearly defines the resource ('platform's built-in system__* template variables'). It distinguishes itself from sibling tools by focusing on the system variable catalog, a unique function.

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

Usage Guidelines5/5

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

The description explicitly states when to use the catalog (to validate placeholders, distinguish variable types) and provides a concrete 'when not' by noting these variables should never be passed in dynamic_variables. This is clear usage guidance with no ambiguity.

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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