learning-mcp

grade_drill

Grade a user's answer on a drilled item and schedule its next review based on the result.

Instructions

Record how the user did, and schedule when the element comes back.

A question has to exist before a verdict can attach to it: either you asked through ask_user, or you asked in chat and pass question and answer here. Grading an element you never actually asked about is rejected.

Args: session_id: From start_topic. element_id: The element that was drilled. verdict: "correct", "partial", or "incorrect". See the standing rules on strictness — when genuinely torn, "partial" is the honest answer. answer: What the user said, in their words. question: Only when you asked in chat instead of through ask_user. notes: What they missed, in a phrase. Worth filling in — it's the only record of how they were wrong.

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": {
      "session_id": "<session_id>",
      "element_id": "<element_id>",
      "verdict": "<verdict>"
    },
    "name": "grade_drill"
  }
}

Input Schema

Name	Required	Description	Default
`notes`	No
`answer`	No
`verdict`	Yes
`question`	No
`element_id`	Yes
`session_id`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 5, 2026

TDQS

A5/5.0

Behavior5/5

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

With no annotations, the description fully shoulders the burden. It discloses the scheduling side-effect, the rejection behavior for never-asked elements, and the semantics of notes as the only record of how the user was wrong. This is rich behavioral context beyond the raw schema.

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 code-formatted Args list is compact and scannable, with a front-loaded purpose sentence and a dedicated prerequisite paragraph. Every sentence contributes either procedural guidance or parameter semantics; no filler is present.

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 6-parameter tool with no output schema and no annotations, the description provides the necessary context: it explains when to use it, all parameters, and the rejection condition. The only potential ambiguity is the exact scheduling behavior, but 'schedule when the element comes back' is sufficient for an agent to invoke correctly.

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

Parameters5/5

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

Schema description coverage is 0%, but the description compensates thoroughly by explaining every parameter: session_id's source, element_id's meaning, verdict's allowed values and guidance, answer's phrasing, question's conditional use, and notes' purpose. This adds significant meaning beyond the bare property names and types.

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 opening sentence 'Record how the user did, and schedule when the element comes back' uses a specific verb and resource, clearly distinguishing this grading/recording tool from siblings like ask_user and next_drill. It goes beyond tautology by stating both the recording and scheduling responsibilities.

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?

It explicitly states the prerequisite that a question must exist before grading, naming ask_user as one valid source and chat with passed question/answer as another. It also gives a clear rejection condition ('Grading an element you never actually asked about is rejected') and advises on verdict strictness, making when-to-use guidance concrete.

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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