Skip to main content
Glama
evergreen-ci

evergreen-mcp-server

Official
by evergreen-ci

download_task_artifacts_evergreen

Download artifacts from a specified Evergreen task, including build outputs, test results, and logs. Artifacts are saved to a local directory structured by version.

Instructions

Download artifacts from a specific Evergreen task. Use this to retrieve build outputs, test results, logs, or other files generated by a task. Artifacts are downloaded to a local directory structure organized by version.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
task_idYesThe ID of the task to download artifacts for. Required.
work_dirNoThe base directory to create artifact folders in. Defaults to 'WORK'.WORK
bearer_tokenNoOverride with a bearer token for this request. If not provided, uses the server's default credentials.
artifact_filterNoOptional filter to download only artifacts containing this string (case-insensitive). If not provided, all artifacts are downloaded.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries full burden. It mentions 'downloaded to a local directory structure organized by version' but does not disclose potential issues like overwrite behavior, permission requirements, rate limits, or error handling for invalid task IDs.

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 sentences, no unnecessary words. First sentence states the purpose, second provides additional context. Very concise and front-loaded.

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?

Although an output schema exists, the description does not mention what the tool returns (e.g., list of downloaded file paths). It only describes the local directory effect. For a download tool, return behavior is important for agents to process results.

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 description coverage is 100%, with all four parameters described adequately. The description adds minimal extra meaning beyond the schema (e.g., 'organized by version'). Baseline is 3 for high 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 clearly states 'Download artifacts from a specific Evergreen task' and lists examples like build outputs, test results, logs. It uses a specific verb and resource, and distinguishes from sibling tools that are read-only get/list operations.

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 tells when to use this tool ('retrieve build outputs, test results, logs, or other files generated by a task') but does not explicitly mention when not to use or compare with sibling tools like get_task_log_* for log content.

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/evergreen-ci/evergreen-mcp-server'

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