get_space_assets

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It explicitly warns that the list is not the whole space, explains it comes from the Catalog API and only includes deployed/exposed objects, and details what will be returned (asset names, descriptions, types, exposure status, consumption URLs, timestamps, counts, pagination info). This is highly transparent and goes beyond a simple listing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured with clear headers (use when, what you'll get, examples, filters, asset types, use cases). It is front-loaded with the core purpose. Some redundancy exists between 'Example queries' and 'Use cases,' but every section contributes meaningfully, so the length is justified.

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

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

For a tool with 4 parameters, no output schema, and no annotations, the description is remarkably complete. It explains the API endpoint, limitations, return content, pagination behavior, asset types, and provides usage examples. It even offers guidance on alternative tools when results are missing, fully contextualizing the tool within its environment.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description substantially enriches parameter understanding. It provides example filter expressions for common use cases (by type, exposed only, name pattern, combined), lists asset types with semantics, and gives example queries. This goes well beyond the schema's terse descriptions, making the filter_expression parameter actionable.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource+scope: 'List all data assets within a specific SAP Datasphere space.' It clearly distinguishes from siblings like list_catalog_assets and search_catalog by emphasizing the space-scoped browsing, and explicitly mentions the alternative list_repository_objects for missing objects.

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

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

A dedicated 'Use this tool when' section lists concrete scenarios, and the description gives explicit exclusion guidance: 'If something you expect is missing, check the repository with list_repository_objects before telling the user it does not exist.' This tells the agent both when to use and when not to use the tool, with an alternative named.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.