semantic_search

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

No annotations provided, so description carries burden. It discloses vector similarity usage and keyword fallback, but does not mention performance, rate limits, or auth requirements. For a search tool, this is adequate but not comprehensive.

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 paragraphs and bullet-like argument list. It is concise, informative, and front-loads the primary functionality. No superfluous 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?

The tool has an output schema (not shown) and 3 parameters. Description covers inputs and core behavior. It does not explain output format, but the output schema likely handles that. Complexity is moderate, and the description is largely complete.

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 0%, and the description effectively explains each parameter: query as natural language, product_name as optional filter, limit as max results. This adds significant value beyond the schema.

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 performs semantic search for port requirements using natural language. It distinguishes itself from sibling tools like search_ports and search_by_port_number by highlighting vector similarity and synonym understanding.

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 example queries and optional filters (product_name, limit). It mentions fallback behavior but lacks explicit guidance on when to use this tool vs alternatives like keyword search.

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