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delete_notebook_record

Remove a specified record from a notebook in DeepTutor learning data. Requires explicit learner authorization and confirmation to prevent accidental deletion.

Instructions

WARNING: Changes stored DeepTutor learning data. Check the target ID and values; call only when the learner explicitly requests this action. Delete a notebook record

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
confirmYesSet true when this write is authorized by the current user request
record_idYesRecord ID
notebook_idYesNotebook ID

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

B3.1/5.0
Behavior1/5

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

The description states 'Changes stored DeepTutor learning data' and 'Delete a notebook record', which clearly indicates a destructive write operation. However, the annotation destructiveHint is false, directly contradicting the description. This is a serious inconsistency, so the description fails to provide transparent behavioral information that aligns with 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 concise, consisting of a warning and a short statement. The warning is front-loaded, which is helpful. It avoids redundancy and is structured logically, though the warning could be considered slightly verbose given the annotation contradiction.

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

Completeness2/5

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

The tool is a deletion operation with an output schema and full parameter documentation. However, the description fails to resolve the contradiction between its destructive warning and the destructiveHint annotation, leaving the agent uncertain about the tool's actual behavior. It also does not clarify prerequisites or expected outcomes beyond the schema, making it incomplete for safe invocation.

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?

The schema already provides full descriptions for all three parameters (notebook_id, record_id, confirm), and schema coverage is 100%. The description adds no new parameter-level details, only generic advice to 'check the target ID and values'. With high schema coverage, 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 clearly states the action: 'Delete a notebook record'. It names the specific verb and resource, and it is distinct from siblings like update_notebook_record or delete_notebook. The warning about changing learning data reinforces the destructive nature without obscuring the core purpose.

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 usage condition: 'call only when the learner explicitly requests this action'. It also advises to check the target ID and values. However, it does not mention alternatives or explicitly state when not to use this tool vs. other notebook-related tools, leaving some inference to the agent.

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