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MarioDeFelipe

SAP Datasphere MCP Server

by MarioDeFelipe
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Python
Remote

get_task_history

Retrieve historical run records for SAP Datasphere task chains to analyze execution patterns, identify failures, and audit runs.

Instructions

Get the execution history for a specific task chain or object in SAP Datasphere.

Use this tool when:

  • Viewing all previous runs of a task chain

  • Analyzing task execution patterns

  • Finding failed runs to investigate

  • Checking historical performance

  • Auditing task chain executions

  • Understanding run frequency and duration

What you'll get:

  • Array of all historical task runs for the specified object

  • Each entry includes: logId, status, startTime, endTime, runTime

  • Sorted by most recent first

  • Shows RUNNING, COMPLETED, FAILED, CANCELLED runs

Required parameters:

  • space_id: The space containing the task chain

  • object_id: The task chain name to get history for

Response includes for each run:

  • logId: Unique identifier for this execution

  • status: RUNNING, COMPLETED, FAILED, or CANCELLED

  • startTime: When the task started (ISO format)

  • endTime: When the task finished (if completed)

  • runTime: Duration in milliseconds

  • objectId: The task chain name

  • applicationId: Always 'TASK_CHAINS' for task chains

  • activity: The activity type (e.g., 'RUN_CHAIN')

  • user: Who initiated the run

Example queries:

  • "Show me the run history for Daily_ETL_Pipeline in SALES_SPACE"

  • "List all executions of Customer_Sync in FINANCE"

  • "Get historical runs for Nested_Chain_1 in DWH_SPACE"

  • "How many times has Data_Refresh run this week?"

Use cases:

  • Identify recurring failures

  • Analyze execution duration trends

  • Find specific failed runs to debug

  • Audit who ran tasks and when

  • Plan maintenance windows

  • Monitor SLA compliance

Workflow example:

  1. Get history: get_task_history(space_id='SALES', object_id='Daily_ETL')

  2. Find failed run: Look for status='FAILED', note logId

  3. Get details: get_task_log(space_id='SALES', log_id=<failed_logId>, detail_level='detailed')

  4. View error messages in the response

Note: Uses API: GET /api/v1/datasphere/tasks/logs/{space_id}/objects/{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 get history for (e.g., 'Daily_ETL_Pipeline', 'Customer_Sync').

Tool Definition Quality

A4.3/5.0
Behavior4/5

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

With no annotations, the description fully discloses the tool's behavior: it returns a sorted array of runs with fields like logId, status, timestamps. It notes the API endpoint and that it's a GET request, indicating a safe read operation.

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-organized with bullet points and sections, but is somewhat lengthy. However, every section adds value, and the most important information is 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 no output schema, the description details the response structure, includes example queries and use cases, and provides a workflow example. It lacks mention of pagination or limits, but is otherwise thorough.

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 with examples, required parameter listing, and context on parameter values (e.g., uppercase space IDs). This goes beyond the schema 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?

The description clearly states the tool fetches execution history for a task chain or object in SAP Datasphere, with a specific verb and resource. It distinguishes from siblings like get_task_log and get_task_status.

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 provides a comprehensive list of when to use the tool, including analyzing runs and finding failures. It gives a workflow example showing integration with get_task_log, but does not explicitly state when not to use it.

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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