get_data_freshnessWhen the dataset was generated and how recently venues were checked: oldest and newest lastVerified dates, counts by age bucket, and the newest check date per city.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It specifies what kind of data the agent receives: oldest and newest lastVerified dates, counts by age bucket, and the newest check date per city. It does not state side-effect behavior or limitations, but for a parameterless 'get' tool, the output contents are the main behavior and they are well specified.
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?
One sentence with a colon introducing a clean list of output components. It front-loads the underlying question and contains no filler, redundant phrasing, or restatement of the title beyond the essential resource.
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 no-parameter, no-output-schema tool, the description covers everything an agent needs: the topic (dataset generation time and venue-check recency) and the exact slices it returns (ranges, bucketed counts, per-city newest check). The absence of an output schema makes this specificity especially valuable.
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 zero properties, so schema description coverage is trivially 100%. There are no parameters for the description to explain, and it correctly spends its words on output semantics. The baseline of 4 for a parameterless tool is appropriate.
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 identifies the resource—dataset freshness—and enumerates the returned information: lastVerified ranges, age-bucket counts, and per-city newest check date. It lacks an explicit verb like 'returns' or 'retrieves' and does not directly reference sibling tools, but its content distinguishes it from get_city_stats, get_venue, list_cities, and search_venues.
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 phrase 'When the dataset was generated and how recently venues were checked' sets a clear use case: an agent needing freshness metadata will know to select this tool. However, it does not explicitly say when not to use it or name alternatives, so the guidance remains implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a clearly distinct purpose: detailed city aggregates, dataset freshness metadata, individual venue lookup, city listing, and filtered venue search. There is no meaningful overlap or risk of confusing one tool for another.
All five tool names follow the same snake_case verb_noun pattern: get_, list_, and search_. This makes the tool surface predictable and easy for an agent to navigate.
Five tools is well-scoped for a read-only venue discovery and analytics server. Each tool earns its place and collectively they cover both raw venue lookup and aggregate city-level insights.
The surface covers venue search, venue detail retrieval, city enumeration, city-level statistics, and data freshness checks. For a read-only directory and analytics domain, this is a complete and coherent set with no obvious dead ends.