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add_notebook_record

Save a learner-authorized lesson or note to a DeepTutor notebook. Provide title, content, and Codex summary to create the record without triggering model calls.

Instructions

WARNING: Changes stored DeepTutor learning data. Check the target ID and values; call only when the learner explicitly requests this action. Save a Codex-produced lesson or note in a DeepTutor notebook; Codex must supply summary to avoid a DeepTutor model call

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesRecord title
outputYesLesson or note content
confirmYesSet true when this write is authorized by the current user request
summaryYesRequired summary written by Codex
user_queryYesLearner question or learning goal
notebook_idYesNotebook ID
record_typeNoDeepTutor record type

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes原始 DeepTutor 工具返回值;其具体字段由对应 DeepTutor API 决定。

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.9/5.0
Behavior4/5

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

Beyond the annotations (readOnlyHint=false, destructiveHint=false), the description warns 'Changes stored DeepTutor learning data' and instructs to 'Check the target ID and values.' It also discloses the consequence of not supplying a summary (a potential DeepTutor model call). These are meaningful behavioral traits not present in the annotations.

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 three sentences, ordered as warning, purpose, and requirement. Each sentence adds distinct value: the warning alerts to side effects, the purpose is clear, and the summary requirement prevents an unintended model call. It could be tightened slightly, but it's efficient and front-loaded with the caution.

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?

Given the tool's complexity (7 params, 6 required) and that an output schema exists, the description covers the key behavioral context: when to call (explicit learner request), what to verify (target ID/values), and a critical requirement (Codex-supplied summary). The schema handles parameter details, so this is sufficiently complete.

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 coverage is 100%, so the baseline is 3. The description adds marginal semantic value for parameters: 'Codex must supply summary' reiterates the schema's summary description, and 'Check the target ID and values' vaguely refers to notebook_id but doesn't add concrete format or selection guidance beyond the schema.

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?

The description states the action with a specific verb and resource: 'Save a Codex-produced lesson or note in a DeepTutor notebook.' This clearly identifies an add operation, and the sibling names (update_notebook_record, delete_notebook_record) make the purpose distinct enough, though the description itself doesn't explicitly contrast with siblings.

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?

The description gives an explicit when-condition: 'call only when the learner explicitly requests this action.' It also adds a prerequisite: 'Codex must supply summary to avoid a DeepTutor model call.' It doesn't mention alternatives like update or delete, but the 'only when' clause provides clear gatekeeping.

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