Google Flights MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool present, there is no possibility of an agent confusing it with another tool. The lone tool is clearly described as the only way to search Google Flights results.
Naming Consistency3/5The single tool name mixes a snake_case provider/domain prefix with a camelCase verb phrase, which is internally inconsistent. Since there is only one tool, there is no broader set of names to establish a consistent pattern, so the score is moderate.
Tool Count3/5A single tool for a flight-search MCP server is borderline; it is not a trivial tool, but the surface feels thin for a server that could plausibly support fare calendars, route metadata, or booking workflows. The count is acceptable but not well-rounded.
Completeness4/5The one tool covers a wide range of search options, including itinerary types, passenger mix, class, bags, price, stops, airlines, time windows, and emissions. For its stated purpose of getting Google Flights search results, it is quite complete, though auxiliary endpoints like airport lookup or flight status are absent.
Average 3.9/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
Tools from this server were used 2 times in the last 30 days.
This repository includes a glama.json configuration file.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure. It states what the search returns and implies a read-only operation, but it does not explicitly mention that no mutations occur, nor does it disclose rate limits, latency differences beyond the per-parameter deepSearch note, or any operational constraints such as result limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core action and uses three focused sentences covering operation, returned data, and use cases. The final use-case list includes some reputation-heavy phrases, but for 31 parameters the summary remains scannable and does not repeat schema content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given 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's enumeration of returned per-itinerary fields is valuable and largely covers the response contract. Input parameters are fully documented in the schema with examples. The main gaps are the lack of pagination/error behavior and some explanation of how booking and departure tokens should be used, but these are partially covered by parameter descriptions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so every parameter is already documented in the input schema. The description only groups parameter types into a summary and adds no new per-parameter semantics, yielding the baseline score for a fully covered schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Searches Google Flights') and a clear resource, and goes beyond the name by enumerating trip types, filter dimensions, and returned data. An agent can immediately understand what this tool does and what its result contract looks like.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit use contexts ('travel-planning agents, fare monitoring, corporate travel dashboards, emission-aware trip optimization') and covers the major itinerary types. There are no sibling tools to compare against, so explicit when-not-to-use or alternative routing is not possible, but the intended use cases are clear.
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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