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record_result

Record a learner's manually graded quiz result as correct, partial, or wrong to award XP, update streaks, schedule spaced repetition, adapt difficulty, and explain the answer.

Instructions

EVALUATE step 2 (only when check_answer said auto_graded=false): record 'correct', 'partial' or 'wrong'. Awards XP, updates streak, schedules spaced repetition, adapts the level. Then explain why the right answer is right, why the tempting wrong one fails, and add one real-world gotcha.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "question_id": "<question_id>",
      "result": "<result>"
    },
    "name": "record_result"
  }
}

Input Schema

Name	Required	Default
`result`	Yes
`user_id`	No	default
`question_id`	Yes
`user_answer`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Nov 2, 2026

TDQS

A4.1/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses major side effects: awards XP, updates streak, schedules spaced repetition, and adapts level. It does not mention idempotency, permissions, or response behavior, but the side-effect disclosure is strong.

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?

The description is front-loaded with the key precondition and recording action. It is compact and mostly free of waste, though the trailing explanation instruction sits somewhat awkwardly alongside the recording behavior.

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

Completeness3/5

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

Given no annotations, no output schema, and four parameters, the description covers when to call and some side effects well. However, it leaves parameter semantics and return behavior largely unspecified, which is a meaningful gap for a mutation tool.

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

Parameters2/5

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

Schema description coverage is 0%, so the description should compensate. It clarifies the allowed values for result ('correct', 'partial', 'wrong'), but says nothing about question_id, user_id, or user_answer, leaving three of four parameters undocumented.

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?

States a specific verb and resource: records the result of the EVALUATE step. It clearly distinguishes this from sibling check_answer by specifying it is used only when check_answer said auto_graded=false.

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?

Explicitly states the precondition: only after check_answer returns auto_graded=false. It also names the allowed result values and implies this is the second step in the evaluation workflow.

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