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PythonicMind2

flight-search-mcp

resolve_airport

Resolve IATA airport codes from city or airport names, enabling flight searches when only names are known.

Instructions

Look up IATA airport codes by city or airport name.

Args: keyword: City or airport name, or exact IATA code (e.g. "London", "Heathrow", "JFK"). Minimum 2 characters. max_results: Max number of matches to return (1-20, default 10).

Use this before search_flights if you only have a city/airport name rather than a 3-letter IATA code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordYes
max_resultsNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior3/5

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 input constraints (minimum 2 characters, max_results range) and core behavior, but does not explicitly state that it is read-only, what happens on no match, or the output format. The description is adequate but lacks deeper behavioral details that an agent might need.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is succinct with two clear paragraphs. The first sentence front-loads the purpose, followed by precise argument definitions in a standard format. No redundant or unnecessary sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description covers the tool's purpose, usage context, and parameter semantics well. The only minor gap is the lack of explicit output structure (e.g., 'returns a list of IATA code strings'), but the verb 'look up' and typical use case imply the result. For a simple lookup tool, it is largely complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must add meaning. It provides rich details for both parameters: keyword examples ('London', 'Heathrow', 'JFK'), minimum length (2 chars), and max_results range (1-20, default 10). This fully compensates for the empty schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('look up') and resource ('IATA airport codes') and specifies the input as 'city or airport name'. It distinguishes itself from siblings (search_flights, resolve_airline) by its specific purpose and the explicit guidance to use it before search_flights.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use the tool: 'Use this before search_flights if you only have a city/airport name rather than a 3-letter IATA code.' This provides clear context and an alternative (direct IATA code usage) without needing additional exclusions.

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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