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SAP Datasphere MCP Server

SAP Datasphere MCP Server

by DimiDR
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

search_catalog

Search across all SAP Datasphere catalog items, including KPIs, assets, spaces, models, views, and tables, using advanced syntax with scoped prefixes and boolean operators.

Instructions

Universal search across all catalog items in SAP Datasphere using advanced search syntax. Supports searching across KPIs, assets, spaces, models, views, and tables. Use SCOPE:<scope_name> prefix for targeted searches. Boolean operators (AND, OR, NOT) supported.

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": {
      "query": "<query>"
    },
    "name": "search_catalog"
  }
}

Input Schema

NameRequiredDescriptionDefault
topNoMaximum number of results to return (default: 50, max: 500)
skipNoNumber of results to skip for pagination (default: 0)
queryYesSearch query with optional SCOPE prefix. Format: 'SCOPE:<scope> <terms>'. Scopes: SearchAll, SearchKPIsAdmin, SearchAssets, SearchSpaces, SearchModels, SearchViews, SearchTables. Example: 'SCOPE:comsapcatalogsearchprivateSearchAll financial'
facetsNoComma-separated list of facets to include or 'all' for all facets. Example: 'objectType,spaceId'
facet_limitNoMaximum number of facet values to return per facet (default: 5)
include_countNoInclude total count of matching results (default: false)
include_why_foundNoInclude explanation of why each result matched (default: false)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.4.0

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses advanced syntax (SCOPE, Boolean operators) and supported scopes, which is useful. However, it doesn't explicitly state read-only behavior, result format, or pagination limitations. The description is adequate but lacks deeper operational context.

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 three sentences, each serving a distinct purpose: stating the tool's scope, listing supported item types, and providing usage syntax. It is front-loaded with the core purpose and contains no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given there is no output schema, the description doesn't explain what the tool returns (e.g., result list format, metadata). It also omits information about result ordering or grouping. The schema covers parameters well, but the lack of return value disclosure leaves a gap for the agent.

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 description coverage is 100%, so the baseline is 3. The description adds a bit of context about query syntax (SCOPE, Boolean operators), but this is also partly in the schema. It doesn't further elaborate on parameters like facets or include_why_found beyond what the schema already provides.

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 performs a 'Universal search across all catalog items' and enumerates supported item types (KPIs, assets, spaces, models, views, tables). This distinguishes it from sibling tools like search_tables (table-specific) and list_catalog_assets (listing, not searching).

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 conveys broad applicability ('Universal search') and provides explicit syntax examples (SCOPE prefix, Boolean operators). While it doesn't explicitly contrast with alternatives like search_tables, the scope is clear enough for an agent to choose it for cross-type searches. No when-not-to-use guidance is given, but the use case is well implied.

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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