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mwaa-mcp-server

by biswasbiplob

get-task-logs

Retrieve Amazon MWAA task instance try logs to debug failed runs. Supports pagination via continuation tokens for large log files.

Instructions

Get logs for a specific task instance try.

    Returns the execution logs for a task instance at a specific try number.
    Useful for debugging failed or problematic task executions.

    Supports pagination for large logs via the full_content and token parameters.
    If the request times out (common with large logs >80MB), automatically retries
    with chunked mode (full_content=false) and returns the first chunk with a
    continuation token for fetching subsequent chunks.

    Args:
        ctx: The MCP context.
        environment_name: Name of the MWAA environment.
        dag_id: The DAG identifier.
        dag_run_id: The DAG run identifier.
        task_id: The task identifier.
        try_number: The try number (starts at 1).
        full_content: Whether to return full content or paginated chunks.
        token: Continuation token for fetching subsequent log chunks.
        map_index: Map index for mapped task instances.
        region: AWS region override.
        profile_name: AWS CLI profile name override.

    Returns:
        CallToolResult with the task logs.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tokenNoContinuation token from a previous response to fetch the next chunk of logs.
dag_idYesThe DAG ID.
regionNoAWS region override.
task_idYesThe task ID.
map_indexNoFor mapped task instances, the map index to get logs for.
dag_run_idYesThe DAG run ID.
try_numberYesThe task try number (starts at 1).
full_contentNoWhether to return full log content. When False, returns chunked logs with a continuation_token for pagination. Defaults to None (server default). Set to False explicitly for large logs.
profile_nameNoAWS CLI profile name override.
environment_nameNoName of the MWAA environment. If omitted and only one environment exists, it is used automatically.
Behavior5/5

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

With no annotations, the description carries the full burden. It discloses pagination behavior via full_content and token, timeout handling (automatically retries in chunked mode for logs >80MB), and the format of responses (CallToolResult with task logs). This is substantial behavioral insight beyond a simple 'returns logs' statement.

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?

The description is well-structured with an opening purpose, a pagination/timeout behavior paragraph, and an Args list. It is slightly verbose due to the Args list duplicating schema properties, but each section adds useful context and the critical details (retry logic, pagination) are front-loaded.

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 10 parameters and no output schema, the description adequately explains the tool's behavior, return type, and edge-case handling (timeouts, large logs). It doesn't cover error cases like nonexistent try_number, but overall it provides a complete picture for an AI agent to invoke the tool correctly.

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 description coverage is 100%, so the baseline is 3. The description's Args section adds context not fully in the schema: try_number starts at 1, full_content implies chunking with continuation_token, and map_index is for mapped task instances. While partially redundant, it reinforces and clarifies parameter semantics.

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 opens with 'Get logs for a specific task instance try' and elaborates 'Returns the execution logs for a task instance at a specific try number.' This specifies the verb (get), the resource (task logs), and the scope (specific task instance try), clearly distinguishing it from siblings like get-task-instance or list-task-instances.

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 explicitly states 'Useful for debugging failed or problematic task executions,' providing clear situational guidance. It doesn't mention exclusions or alternatives, but no other sibling tool retrieves logs, so this is clear context without needing explicit when-not-to-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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