browse_marketplace

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

Since no annotations are provided, the description carries the full burden. It discloses the read-only nature implicitly through 'Browse and search' and details the expected output fields (package IDs, names, descriptions, categories, provider info, versions, sizes, pricing). It does not mention side effects, authentication requirements, or limitations, but for a non-mutating marketplace browse tool, this is acceptable and adds meaningful context.

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 well-structured with clear sections (when to use, what you'll get, categories, examples, use cases). It is front-loaded with the main purpose, and every section provides actionable information. There is no filler or redundant content.

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?

Although the tool is simple (2 optional params, no output schema), the description is comprehensive. It explains the tool's scope, typical use cases, example queries, and the structure of returned data. For an AI agent, it provides enough context to decide when and how to invoke the tool without additional information.

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?

The input schema already documents both parameters with descriptions, and schema coverage is 100%. The description adds value beyond the schema by supplying example queries and categories that clarify how to use the filters (e.g., 'currency' or 'benchmark' for search_term). This helps the agent map natural language to parameter values.

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 clearly states the tool's purpose with a specific verb and resource: 'Browse and search available data packages in the SAP Datasphere marketplace.' It is distinctly different from sibling tools, which focus on connections, tables, queries, and administrative operations. The description also enumerates the types of data returned, further reinforcing its unique purpose.

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?

The description includes an explicit 'Use this tool when:' section with concrete scenarios (e.g., 'What data packages are available?', 'Looking for external reference data'). It also provides example queries and use cases. However, it does not explicitly mention when not to use the tool or any sibling alternatives, so it stops short of full guidance.

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