Resolve a free-text city or airport name to airport codes (e.g. 'london' → LHR, LGW, STN, LTN, LCY). Use before other tools when the user gives city names. airport_code is the 3-letter IATA airport code.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
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?
No annotations are provided, so the description carries the full burden. It discloses that the tool handles free-text input, returns multiple possible codes, and uses 3-letter IATA codes. However, it does not mention behavior for ambiguous queries, unrecognized input, or the exact return structure, leaving gaps for an agent relying solely on this text.
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 two concise sentences with a front-loaded purpose, an illustrative example, and a clear usage instruction. Every sentence contributes value without redundancy.
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 one-parameter resolver with no annotations and no output schema, the description covers the core purpose, input semantics, output format (IATA codes), and when to use it. It could be more explicit about the exact response shape or error behavior, but the essential call-making information is present.
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 only parameter, 'query', has no schema description (0% coverage), so the description must compensate. It does so meaningfully by specifying that the query is a free-text city or airport name and by giving a concrete example. This is sufficient for a single simple parameter.
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 ('Resolve a free-text city or airport name to airport codes') with a concrete example ('london' → LHR, LGW, STN, LTN, LCY). It clearly distinguishes this from sibling tools by positioning it as a prerequisite step before other tools.
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 explicitly says to use this tool 'before other tools when the user gives city names,' which gives clear context for when it applies. It does not name specific alternative tools or list exclusion cases, but the instruction is sufficiently directive for typical airport-query workflows.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool has a clearly distinct purpose: resolving place names, listing one route's details, listing all destinations from a hub, and finding multi-leg connections. The descriptions explicitly position airport_destinations as the broader alternative to route_detail, removing ambiguity.
All names are lowercase snake_case and readable, but they mix noun phrases (airport_destinations, route_detail) with imperative verbs (find_connections, resolve_airport). A more consistent verb_noun pattern like list_airport_destinations or get_route_detail would improve predictability.
Four tools is well-scoped for a flight-route data server: resolving input, inspecting a single route, expanding a hub, and planning connections. Each tool serves a distinct query type without unnecessary bloat.
The core workflows of route lookup, hub exploration, and connection finding are covered. Minor gaps exist, such as no direct listing of inbound routes to an airport or airline-specific search, but these are workarounds via existing tools.