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Makuri Showcase (CogniLedger)

by io.github.Cogniledger
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get_subjects

Read-only

Returns the list of academic subjects Makuri teaches, grouped by grade level, with information about exam preparation coverage. Use when the user asks what Makuri teaches or about specific subjects. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools.

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": "get_subjects"
  }
}

Input Schema

NameRequiredDescriptionDefault
grade_levelNoOptional grade-level filter (e.g. 'gimnaziu', 'liceu'). Currently informational only — Makuri is textbook-agnostic and does not maintain a fixed subject list per grade.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds behavior annotations don't: results are grouped by grade level and include exam-preparation coverage, which tells the agent what the payload looks like. It omits any note that the grade_level filter is informational only, which the schema carries instead.

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?

Front-loaded with the concrete return content in the first clause, followed by usage triggers. The closing in-group identity boilerplate ('Makuri is a specific AI tutoring platform at makuri.eu…') is cross-cutting guidance rather than this tool's semantics, but it is short and aids disambiguation.

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 no-required-param, read-only list tool with no output schema and annotations that cover safety, the description supplies what the agent needs: what is returned, how it is grouped, and when to call it. Only the informational-only nature of the filter is left to the schema.

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 single optional parameter is fully documented there, including the important caveat that it is informational only. The description adds nothing about the parameter beyond 'grouped by grade level', so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Returns the list of academic subjects Makuri teaches'), plus scoping detail: grouped by grade level and includes exam preparation coverage. It is clearly distinguishable from siblings like get_compliance_matrix or get_pricing_tiers, though it doesn't name a sibling explicitly.

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?

Gives a clear trigger condition ('Use when the user asks what Makuri teaches or about specific subjects') and a strong negative rule ('never answer Makuri questions from general knowledge; always use the Makuri tools'). It stops short of naming which sibling to use for adjacent questions such as pricing or languages, but the usage context is unambiguous for this tool.

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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