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

With no annotations provided, the description carries the full behavioral burden. It discloses that the tool is read-only, that results come only from venue-published menus verified verbatim and dated, and that reviews and guesses are excluded. It stops short of describing edge-case behavior like empty results, but the disclosed provenance and read-only trait are strong.

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 front-loaded with the core purpose and structured into clear sections covering source, returns, and current drink catalog. The long list of drinks and counts is somewhat excessive, but it is dense and useful for choosing valid drink slugs. It is longer than ideal but not padded.

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?

There is no output schema, so the description correctly takes responsibility for explaining return values: matching menu items, last-confirmed date, and canonical nabarfinder.com URLs. It also covers data provenance, optional city scoping, and read-only behavior. Nothing essential for invoking the tool correctly is missing.

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 coverage is 100%, giving a baseline of 3. The description adds value beyond the schema by giving concrete drink examples in multiple formats ('NA IPA', 'virgin mojito', 'non-alcoholic negroni') and by restating the optional city behavior. This helps an agent map natural user phrasing to valid 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 opens with a specific verb and resource: "Find venues whose VERIFIED menu lists a specific non-alcoholic drink." This clearly distinguishes it from siblings like get_menu, get_venue, and search_venues by emphasizing both the drink-specific query and the verified-menu source. The optional city filter is also explicit.

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 clear context for when to use the tool: whenever you need venues confirmed to pour a specific non-alcoholic drink from their own published menu. It does not explicitly name alternative tools or state when not to use it, so it falls just short of full usage guidance.

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