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evergreen-ci

evergreen-mcp-server

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by evergreen-ci

get_patch_failed_jobs_evergreen

Analyze failed CI/CD jobs for a patch to debug build failures, showing failed tasks, timeout issues, and test failure counts with log links.

Instructions

Analyze failed CI/CD jobs for a specific patch to understand why builds are failing. Shows detailed failure information including failed tasks, build variants, timeout issues, log links, and test failure counts. Essential for debugging patch failures. If project_id is not specified, will automatically detect it from your workspace directory and recent patch activity.This tool may return a list of available project_ids if it cannot determine the project_id automatically.You should ask the user which project they want to use, then call this tool again with the project_id parameter set to their choice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
patch_idYesPatch identifier obtained from list_user_recent_patches. This is the 'patch_id' field from the patches array.
project_idNoEvergreen project identifier for the patch. If not provided, will auto-detect.
max_resultsNoMaximum number of failed tasks to analyze. Use 10-20 for focused analysis, 50+ for comprehensive failure review.
bearer_tokenNoOverride with a bearer token for this request. If not provided, uses the server's default credentials.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Discloses auto-detection behavior for project_id, potential return of project list, and recommends user interaction. While no annotations are present, the description covers key behavioral aspects; however, it could note that the tool is read-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise (4-5 sentences) and front-loaded with purpose. Each sentence adds value, but it could be slightly tighter. Overall well-structured.

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 existence of an output schema, the description effectively covers what the tool does, the parameters, and edge cases (missing project_id). It is complete for an analysis tool with moderate complexity.

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?

Adds meaning beyond the 100% schema coverage by explaining the purpose of patch_id and project_id, and providing usage guidance for max_results ('10-20 for focused analysis, 50+ for comprehensive review'). This adds value for the agent.

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 the tool's purpose: 'Analyze failed CI/CD jobs for a specific patch to understand why builds are failing.' It specifies the verb (analyze) and resource (failed CI/CD jobs) and is distinct from sibling tools that focus on individual tasks or test results.

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 explicit guidance on when to use ('Essential for debugging patch failures') and how to handle missing project_id (ask user to specify). It does not explicitly exclude scenarios but gives sufficient context for appropriate use.

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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