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SAP Datasphere MCP Server

SAP Datasphere MCP Server

by DimiDR
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_asset_variables

Retrieve input parameters, defaults, and filter annotations from a SAP Datasphere asset's OData metadata to know which variables to bind before querying.

Instructions

Retrieve input parameters/variables and filter-capability annotations declared in the OData $metadata of a SAP Datasphere asset (wave 2026.10). Use this when the asset is parameterised (e.g., a view or analytic model with input variables) and you need to know what variables to bind and which fields are filterable/sortable before querying. Returns variables (name, type, default, nullable, multi_value), filter annotations, and the column list.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "space_id": "<space_id>",
      "asset_id": "<asset_id>"
    },
    "name": "get_asset_variables"
  }
}

Input Schema

NameRequiredDescriptionDefault
asset_idYesAsset identifier (view or analytic model exposed for consumption)
space_idYesSpace identifier (e.g., 'SAP_CONTENT')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.4.0

TDQS

A4.2/5.0
Behavior4/5

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

There are no annotations, so the description carries the full burden. It discloses the source ($metadata) and return payload (variables, filter annotations, column list) with variable attributes. This is adequate for a read-only metadata retrieval operation, though it stops short of stating side-effect absence or potential errors.

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?

The description is concise: two sentences that front-load the action, then provide usage guidance and a summary of return values. Every sentence earns its place without redundancy.

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?

The description covers the main use case (pre-query variable discovery), return fields, and the metadata source. It omits details on filter annotation structure or the meaning of 'wave 2026.10', but for an agent deciding whether to invoke the tool, the information is sufficient.

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 descriptions already cover both parameters (asset_id and space_id) with 100% coverage, so the description adds only marginal semantic context by referring to 'asset' and 'parameterised'. It does not further explain the parameters' relationship or any constraints beyond the 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?

The description clearly states the tool retrieves input variables and filter-capability annotations from the OData $metadata of a SAP Datasphere asset. It specifies the exact resource (asset metadata) and distinguishes itself from sibling tools like get_asset_details or get_analytical_metadata by focusing on parameters and filter annotations.

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?

It provides explicit usage context: 'Use this when the asset is parameterised' and before querying to know bindable variables and filterable fields. However, it does not name alternative sibling tools for non-parameterised assets, so the guidance is clear but not fully comparative.

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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