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DimiDR

SAP Datasphere MCP Server

by DimiDR

run_task_chain

Initiate a SAP Datasphere task chain to execute ETL pipelines and data transformations, returning a log ID for asynchronous progress tracking.

Instructions

Execute a task chain in SAP Datasphere and get a log ID for tracking.

Use this tool when:

  • User asks to "Run the ETL pipeline" or "Execute task chain X"

  • Triggering scheduled data loads or transformations

  • Starting data replication or synchronization jobs

  • Automating data refresh workflows

  • Executing orchestrated data pipelines

What happens:

  • Task chain execution is initiated immediately

  • Returns a logId to track the execution status

  • Task runs asynchronously (use get_task_log to check status)

  • All child tasks in the chain are executed in order

Required parameters:

  • space_id: The space containing the task chain (e.g., 'SALES_SPACE')

  • object_id: The task chain name/ID (e.g., 'Daily_ETL_Pipeline')

What you'll get:

  • logId: Unique identifier to track this execution

  • Use get_task_log(space_id, logId) to monitor progress

  • Use get_task_history(space_id, object_id) to see all runs

Example queries:

  • "Run the Daily_ETL_Pipeline in SALES_SPACE"

  • "Execute task chain Customer_Sync in FINANCE_SPACE"

  • "Trigger the data refresh pipeline in ANALYTICS"

  • "Start the nightly batch job in DWH_SPACE"

Important notes:

  • Task chains run asynchronously - tool returns immediately

  • Check status with get_task_log using the returned logId

  • Requires appropriate permissions to run task chains

  • Failed runs can be investigated with detailed logs

Workflow example:

  1. Run task chain: run_task_chain(space_id='SALES', object_id='Daily_ETL')

  2. Get logId from response (e.g., 2295172)

  3. Check status: get_task_log(space_id='SALES', log_id=2295172)

  4. View details: get_task_log(space_id='SALES', log_id=2295172, detail_level='detailed')

Note: Uses API: POST /api/v1/datasphere/tasks/chains/{space_id}/run/{object_id}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
space_idYesThe space ID containing the task chain (e.g., 'SALES_SPACE', 'FINANCE'). Must be uppercase.
object_idYesThe task chain name/identifier to execute (e.g., 'Daily_ETL_Pipeline', 'Customer_Sync').
Behavior5/5

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

No annotations are provided, but the description fully discloses asynchronous behavior, immediate return of a logId, ordered child task execution, permission requirements, and the ability to investigate failures. This goes beyond basic expectations for a run-triggering tool.

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?

Although the description is long, it is meticulously structured with labeled sections: when to use, what happens, required parameters, output, examples, and workflow. Every sentence provides useful information, making it efficient despite its length.

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?

The tool has async behavior, a logId output, and related sibling tools. The description covers initiation, what to expect, how to track via get_task_log, how to view history via get_task_history, and even the underlying API endpoint. This is complete for a complex trigger tool.

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 adds value by providing concrete examples for space_id ('SALES_SPACE') and object_id ('Daily_ETL_Pipeline') plus a workflow example, reinforcing the practical meaning beyond the schema's own descriptions.

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 'Execute a task chain in SAP Datasphere and get a log ID for tracking' with a specific verb and resource. It also differentiates from sibling tools by naming get_task_log for status and get_task_history for history, making the purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides an explicit 'Use this tool when' list with concrete trigger phrases, and in 'What you'll get' explicitly directs to get_task_log and get_task_history as follow-up tools. This completely answers when and how to use it versus alternatives.

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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