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lesson_remove

DestructiveIdempotent

Remove a materialized lesson by slug. Protected lessons require foreground writer force; pass replacement_slug to redirect inbound wikilinks to an umbrella lesson.

Instructions

Remove one materialized lesson section by slug.

Refuses protected lessons unless force=True is called from a foreground writer. Curator/evolve cleanup may pass force accidentally or maliciously; non-foreground force is ignored. Pass replacement_slug when this is a consolidation to redirect every inbound [[wikilink]] to the umbrella lesson; without it, the successful response lists all dangling sources.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYes
forceNo
replacement_slugNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changedv0.17.0
    • addedInput schema / properties / replacement_slug
      Added value: +{
      +  "default": "",
      +  "title": "Replacement Slug",
      +  "type": "string"
      +}
  2. First observedv0.13.2

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the destructiveHint annotation, the description discloses meaningful behavioral details: refusal of protected lessons, foreground-writer enforcement, ignoring non-foreground force, wikilink redirection via replacement_slug, and dangling-source reporting. This goes well beyond what annotations alone provide.

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 description is dense but every sentence adds value: singular purpose, force constraint, attack/misuse vector, and consolidation behavior. It is front-loaded with the primary action and avoids filler.

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?

Given the tool's destructive nature and the presence of an output schema, the description fully covers the necessary behavioral context: when removal succeeds, when it refuses, how force works, and what happens with wikilinks. No significant gap remains for an agent to invoke it 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%, so the description carries the full burden of explaining parameters. It explains slug (target section), force (foreground writer requirement), and replacement_slug (consolidation redirection and dangling source behavior). All three parameters are semantically covered.

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 opens with a specific verb and resource: 'Remove one materialized lesson section by slug.' This clearly distinguishes the tool from read/restore/patch siblings like lesson_get, lesson_restore, and lesson_patch.

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 clear operational context: protected lessons require force=True from a foreground writer, and non-foreground force is ignored. It also explains when to pass replacement_slug for consolidations. It does not explicitly name alternative tools, but the usage conditions are strong.

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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