SAP AI Core Documentation MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool as listing categories and contents, which implies a read-only operation, but doesn't specify details like whether it returns structured data, pagination behavior, or potential rate limits. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 concise with two sentences that are front-loaded: the first states the purpose, and the second provides usage context. There's no wasted text, and it efficiently conveys key information. It could be slightly improved by integrating the usage hint more seamlessly, but overall it's well-structured.

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?

Given the tool's complexity is low (0 parameters, no output schema), the description is somewhat complete but has gaps. It explains what the tool does and its usefulness, but without annotations or output schema, it lacks details on behavioral traits like return format or limitations. For a simple list tool, this is adequate but not fully comprehensive.

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 has 0 parameters with 100% coverage, so no parameters need documentation. The description adds value by clarifying the scope ('all available documentation categories and their contents'), which isn't captured in the schema. This compensates well for the lack of parameters, making it clear what the tool returns without needing input.

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: 'List all available documentation categories and their contents.' It specifies the verb ('List') and resource ('documentation categories and their contents'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_ai_core_document' or 'search_ai_core_docs', which might also involve documentation retrieval.

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 provides some usage guidance with 'Useful for exploring what documentation is available,' implying this tool is for discovery or browsing. However, it doesn't explicitly state when to use this tool versus alternatives like 'search_ai_core_docs' or 'get_ai_core_topic,' leaving the agent to infer based on context. No exclusions or prerequisites are mentioned.

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