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MarioDeFelipe

SAP Datasphere MCP Server

by MarioDeFelipe
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Python
Remote

get_task_log

Retrieve detailed information about a task execution, including status, logs, and error messages, to monitor progress or debug failures in SAP Datasphere.

Instructions

Get detailed information about a specific task execution in SAP Datasphere.

Use this tool when:

  • Checking status of a running task (after run_task_chain)

  • Investigating why a task failed

  • Viewing detailed execution logs and messages

  • Monitoring task chain progress

  • Debugging data pipeline issues

What you'll get (depends on detail_level):

  • status (default): Simple status object {"status": "COMPLETED"}

  • status_only: Just the status string "COMPLETED"

  • detailed: Full details including messages and child nodes

  • extended: Extended logs with complete message details

Required parameters:

  • space_id: The space where the task ran

  • log_id: The log ID from run_task_chain or get_task_history

Optional parameters:

  • detail_level: Amount of detail to return

    • 'status' (default): Status object only

    • 'status_only': Status string only

    • 'detailed': Full logs with messages and children

    • 'extended': Extended logs with message details

Status values:

  • RUNNING: Task is currently executing

  • COMPLETED: Task finished successfully

  • FAILED: Task encountered an error

  • CANCELLED: Task was manually stopped

Example queries:

  • "Check status of task log 2295172 in SALES_SPACE"

  • "Get detailed logs for log ID 2295172"

  • "Show me why task 2326060 failed in FINANCE"

  • "Get extended execution details for log 2295172"

Detailed response includes:

  • logId, status, startTime, endTime, runTime

  • objectId (task chain name)

  • user who ran the task

  • children: Array of child task executions

  • messages: Array of log messages with severity and timestamps

Use cases:

  • Monitor long-running ETL jobs

  • Debug failed data pipelines

  • Audit task execution history

  • Track data refresh timing

  • Investigate error messages

Note: Uses API: GET /api/v1/datasphere/tasks/logs/{space_id}/{log_id}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
space_idYesThe space ID where the task ran (e.g., 'SALES_SPACE', 'FINANCE'). Must be uppercase.
log_idYesThe log ID to retrieve details for (obtained from run_task_chain or get_task_history).
detail_levelNoLevel of detail to return. Options: 'status' (default), 'status_only', 'detailed', 'extended'.status

Tool Definition Quality

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the API endpoint, response structure per detail_level, status values, and example queries. It does not mention rate limits or auth, but for a read operation, the provided information is sufficient and transparent.

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 longer but well-structured with clear sections (use cases, parameters, status values, example queries). It front-loads the purpose and organizes information logically. While some redundancy exists (e.g., repeating detail_level options), it remains efficient for the complexity.

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 no output schema, the description compensates by detailing the response for each detail_level, status values, and providing example queries. It covers use cases and prerequisites. The tool's complexity (multiple detail options) is addressed adequately.

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%, but the description adds value beyond the schema: it specifies that space_id must be uppercase, log_id is obtained from specific tools, detail_level defaults to 'status', and explains each option. This extra context helps the agent use parameters correctly.

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: 'Get detailed information about a specific task execution'. It uses a specific verb+resource and distinguishes from siblings like get_task_history (which lists logs) and get_task_status (which likely returns simpler status) by specifying that log_id comes from those tools and offering multiple detail levels.

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 lists when to use the tool with bullet points (e.g., checking status, investigating failures, monitoring progress). It does not directly exclude alternatives, but the use cases are specific enough to guide selection. The note about log_id source (from run_task_chain or get_task_history) provides necessary context.

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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