Skip to main content
Glama

skill_trash_user_skill

Move a user-created skill to a trash folder instead of permanently deleting it. Requires explicit confirmation (force=true) and rejects bundled skills.

Instructions

function_purpose: Move a user-created skill directory into a trash location instead of hard deleting it.

Policy:

  • Only user-created skills may be trashed. Bundled/Anthropic skills are rejected.

  • The skill directory is moved under a trash/skills subdirectory with a timestamped folder name.

  • All operations are logged to an operations log file.

Args:

  • name: str Skill name to trash

  • force: bool Require explicit confirmation flag (default True). If False, the call is a dry refusal.

Returns:

  • dict[str, Any] with:

    • trashed: bool

    • name: str

    • trash_path: str | None

    • message: str

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
forceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description fully carries the behavioral disclosure burden. It discloses that the operation moves rather than deletes, uses a timestamped trash path, logs operations, and rejects non-user-created skills—far beyond minimal expectations.

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 front-loaded with a clear purpose statement, followed by concise policy, args, and return sections. Every section provides necessary information without filler or redundancy.

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?

The description covers the operation's behavior, constraints, argument semantics, and return value shape. Even with no annotations and a minimal schema, an agent has enough information to invoke the tool correctly and interpret results.

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?

The input schema only provides name and force with types/defaults. The description adds meaningful semantics: name identifies the skill to trash, and force requires explicit confirmation with False producing a dry refusal. This fully compensates for the 0% schema description coverage.

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 states a specific action: moving a user-created skill directory into a trash location rather than hard deleting it. It clearly differentiates from the sibling skill_trash_user_asset tool by targeting skills specifically.

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 provides explicit policy: only user-created skills may be trashed and bundled/Anthropic skills are rejected. It also explains the force flag behavior, but it does not explicitly name alternatives for trashing assets, so it stops short of full alternative guidance.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/tsoernes/skills-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server