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evergreen-ci

evergreen-mcp-server

Official
by evergreen-ci

get_task_log_detailed

Retrieve full, untruncated task logs for debugging setup errors, timeouts, and compilation failures. Includes automated error pattern analysis with top terms and example lines.

Instructions

Get the complete raw task logs via REST API. Returns the full untruncated task execution log including timeout handler output, process dumps, and stdout/stderr — content that the GraphQL get_task_log_summary tool cannot access. Automatically scans for error patterns and returns a structured summary with top error terms and example lines when errors are found. Best for debugging non-test failures (setup errors, timeouts, compilation failures). Use task_id from get_patch_failed_jobs results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
task_idYesTask identifier from get_patch_failed_jobs response. Found in the 'task_id' field of failed_tasks array.
bearer_tokenNoOverride with a bearer token for this request. If not provided, uses the server's default credentials.
execution_retriesNoTask execution number if task was retried. Usually 0 for first execution, 1+ for retries.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Discloses automatic error pattern scanning and structured summary, but with no annotations, more could be added (e.g., response size, auth requirements, destructive potential). No contradiction with annotations.

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?

Four sentences, front-loaded with purpose, no redundancy. Every sentence adds value.

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

Completeness4/5

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

Given output schema exists (not shown) and no annotations, description sufficiently covers return value (full logs + error summary). Slightly lacking in response size disclosure, but adequate for the tool's complexity.

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?

Schema coverage is 100%, so description adds marginal value. It reinforces task_id source but does not provide new meaning beyond schema.

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?

Description clearly states it gets complete raw task logs via REST API, and distinguishes from the GraphQL get_task_log_summary tool by listing content it can access (timeout handler output, process dumps, stdout/stderr).

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?

Explicitly recommends use for debugging non-test failures and references get_patch_failed_jobs as source for task_id. Does not explicitly state when not to use or provide alternatives for test failures, but the differentiation from get_task_log_summary is clear.

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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