List Covered Cities
list_citiesUse this when a user asks about Dizko city coverage or freshness.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
list_citiesUse this when a user asks about Dizko city coverage or freshness.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the agent knows this is a safe, complete-list read. The description adds the coverage/freshness concept but does not explain what freshness means or how the returned city list behaves. No contradiction exists, but behavioral disclosure beyond annotations is minimal.
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 a single, focused sentence with no redundant wording. It front-loads the intended trigger and stays minimal, which is appropriate for a zero-parameter tool.
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 tool as simple as listing covered cities, the description is mostly complete. However, there is no output schema and the description does not specify what "freshness" includes or what exact information will be returned, leaving some ambiguity for an agent trying to answer freshness-related questions.
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 tool has zero parameters, and schema description coverage is effectively 100%, so there are no parameter semantics to clarify. The baseline for a no-parameter tool is 4, and the description does not need to add anything further.
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 states a specific action context: "when a user asks about Dizko city coverage or freshness," and the title identifies the resource as covered cities. It is clear enough, though it does not explicitly distinguish itself from get_city_pulse or other city-related siblings beyond the coverage/freshness use case.
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 gives explicit trigger conditions: "Use this when a user asks about Dizko city coverage or freshness." It provides useful when-to-use guidance, though it does not mention any alternatives or when explicitly not to use it.
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.
Most tools map to distinct actions across search, recommendations, preferences, tickets, and feedback, with clear descriptions. The only real ambiguity is between recommend_events and recommend_events_for_user, plus some follow-up overlap, but boundaries are mostly clear.
Tool names follow a consistent imperative verb_noun pattern and use snake_case throughout. Minor inconsistency exists in event vs events pluralization and interchangeable create/save verbs, but the overall pattern is predictable.
23 tools is a large surface for an events assistant, landing in the 16-25 range that feels heavy. The count is defensible given the breadth of features, but it is borderline and somewhat difficult to navigate.
The set covers the core consumer journey well: search, recommendations, preference onboarding and CRUD, ticket quoting and purchase, and post-event feedback. Missing order-status or refund/cancellation endpoints are minor gaps, but primary workflows do not hit dead ends.