cc_semantic_search

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

No annotations are provided, so the description carries full burden. It discloses that the vector index builds in the background (~60 seconds) and that results are ranked by semantic similarity score. While it doesn't explicitly state read-only behavior, it implies a non-destructive search. The background index build is a useful behavioral detail.

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?

Three sentences, no wasted words. First sentence states purpose and method. Second gives usage context and examples. Third mentions initialization latency and return structure. Efficient and well-organized.

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 search tool with 4 parameters, no output schema, and no annotations, the description covers the core functionality and usage. It could mention that the first call may be slow due to background indexing, but it already hints at that. Adequate for an AI agent to correctly invoke the tool.

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?

Schema description coverage is 100%, so baseline is 3. The description adds value by providing concrete examples for the query parameter and clarifying the top_k range and response_format options. However, it largely restates the schema descriptions, so the added value is moderate.

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 it performs natural language semantic search across ~200,000+ ABAP objects using AI embeddings. The verb 'search' and resource 'ABAP objects' are specific, and the semantic approach distinguishes it from sibling tools like cc_lookup_object (exact name) or cc_search_by_component (component-based).

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?

Explicitly says 'Use this when you know WHAT the API should do but not its exact name' and gives concrete example queries. This provides clear usage context and implies alternatives (e.g., use exact-name tools when name is known).

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