Skip to main content
Glama

colab_fs_remove

Remove files or directories in a Colab runtime. Use recursive for non-empty directories and missing_ok to treat absent paths as success.

Instructions

Remove explicitly. After response loss, stat first; use missing_ok for idempotent cleanup.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesRuntime path confined to /content; relative paths resolve under /content.
sessionNoTracked session name. Null is allowed only when exactly one session exists.
recursiveNoRequired to remove a non-empty directory; default false.
missing_okNoTrue treats an absent path as success; default false.
Behavior3/5

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

With no annotations provided, the description carries the full burden of disclosing destructive behavior. It implies permanence by instructing to stat after response loss, but does not explicitly state that the operation is irreversible or what side effects occur (e.g., non-empty directory requires recursive flag). The guidance is useful but not a complete disclosure of behavioral traits.

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?

Two short sentences deliver essential operational guidance without any filler. Every word contributes to the intended usage, making it highly concise and well-structured.

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

Completeness3/5

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

The tool is a destructive operation with no output schema, so the description should explain what the user can expect on success/failure. It omits return value information and does not mention that recursive is needed for directories (though schema covers that). The stat-first advice helps, but the description leaves gaps about error handling and outcome confirmation.

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

Parameters4/5

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

The schema covers 100% of parameters with descriptions, so the baseline is 3. The description adds value by explicitly tying missing_ok to idempotent cleanup and suggesting stat-first behavior, which enhances understanding of when to set that parameter beyond the mere schema text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Remove explicitly' clearly indicates the core action (remove) and resource (file system), but is vague about what exactly can be removed (files, directories, both). It does not distinguish itself from sibling tools like colab_fs_move or colab_fs_mkdir, relying on the tool name for specificity.

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?

Provides clear context on when to use missing_ok for idempotent cleanup and advises running stat first after response loss, which is a safety check. However, it does not explicitly state when this tool should be chosen over alternatives or when not to use it, so it misses the full exclusion 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/anluin/colab-mcp'

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