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rokrokss
by rokrokss

download_attachment

Fetch uploaded files from GitLab by project, secret, and filename. Images are returned inline as base64 for viewing; non-images are saved to disk. Use local_path to save images to disk.

Instructions

Download an uploaded file from a GitLab project by secret and filename. Image files (png, jpg, gif, webp, svg, bmp, ico) are returned inline as base64 image content so the AI can view them directly. Non-image files are saved to disk. Use local_path to force saving image files to disk instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
secretYesThe 32-character secret of the upload
filenameYesThe filename of the upload
local_pathNoLocal path to save the file (optional, defaults to current directory)
project_idYesProject ID or URL-encoded path of the project

Schema Changelog

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

  1. First observedv2.0.35

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It honestly states that image files return as base64 content, non-image files are saved to disk, and local_path overrides the default behavior. It does not detail auth requirements or overwrite behavior, but the core side effects are clear.

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 three concise sentences with no filler. It front-loads the primary action, then explains the key behavioral branches, and ends with the override option. Every sentence earns its place.

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 description explains the input mechanism and the main behavioral outcomes, but with no output schema, it never states what the tool returns for non-image files once they are saved to disk (e.g., a path or confirmation). This is a meaningful gap for an agent that needs to use the downloaded file later.

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?

Schema coverage is 100%, so the baseline is 3, and the description adds meaningful parameter behavior: local_path can force saving image files to disk. It also clarifies that secret and filename are the identifying keys for the upload, adding context beyond the raw schema field names.

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 verb and resource: 'Download an uploaded file from a GitLab project by secret and filename.' It also clarifies the unique mechanism (secret-based uploads) and distinguishes image from non-image handling, which separates it from sibling tools like download_release_asset or download_job_artifacts.

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 clearly explains when files are returned inline versus saved to disk, and explicitly tells the agent to use local_path when saving image files to disk is desired. It does not explicitly name alternative tools or state when not to use this tool, so it falls short of a 5.

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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