chaoslimba-mcp-server

Get Suggested Questions

cl_get_suggested_questions

Read-only

Retrieve AI tutor conversation starter questions filtered by CEFR level or category to audit prompt quality and topic coverage.

Instructions

Returns AI tutor conversation starter questions, optionally filtered by CEFR level or category. Useful for auditing prompt quality and topic coverage.

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": "cl_get_suggested_questions"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Max results (default 30)
`category`	No	Filter by category
`cefrLevel`	No	Filter by CEFR level

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 30, 2026

TDQS

A3.8/5.0

Behavior3/5

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

The readOnlyHint=true annotation already establishes this as a safe read operation. The description adds useful context about what is returned and that filtering is possible, but it does not disclose return format, ordering, or any behavior beyond what the schema parameters imply. This is moderate but not rich behavioral 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 two sentences with zero fluff. The first sentence front-loads the action and resource, and the second sentence adds a practical use case. Every clause earns its place.

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?

For a simple read-only getter with three optional parameters and no output schema, the description covers what is returned, the filter options, and the intended use. The schema handles parameter constraints and the annotation covers safety, so an agent has enough to invoke the tool correctly. Minor gaps like return structure are acceptable given the tool's simplicity.

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%, so all three parameters already have clear descriptions, defaults, and constraints. The description only restates the filtering capability without adding extra semantics such as accepted category formats or how limit interacts with filters, so the baseline of 3 is appropriate.

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 states a specific verb ('Returns'), a clear resource ('AI tutor conversation starter questions'), and the optional filters (CEFR level or category). This resource is distinct from siblings like cl_get_reading_questions or cl_get_tutor_openings, so an agent can tell them apart without inspecting schemas.

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 provides a use case ('auditing prompt quality and topic coverage') that hints at when to use it. However, it does not explicitly name alternatives or explain when not to use this tool, leaving the agent to infer the distinction from the resource name.

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