Google Flights MCP

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: search_flights and search_multi_city handle different search modes, get_return_flights advances itineraries, get_booking_options retrieves booking providers, and the price-tracking tools (track, list, untrack, check, get_history) cover the full watch lifecycle without overlap.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern, such as search_flights, track_price, get_price_history, and untrack_price. Verb choices are appropriate and parallel (track/untrack, get/list/check), making the set predictable and easy to navigate.
Tool Count5/5
Nine tools is well-scoped for the server's purpose of flight search and price tracking. Every tool serves a clear need, and there is no bloat or redundancy.
Completeness4/5
The tool surface covers the entire flight discovery flow (search, multi-city, return options, booking) and price watch CRUD operations. The only minor gap is the lack of an update operation for an existing price watch, but users can untrack and re-track to adjust targets, so it is easily worked around.
Average 4/5 across 9 of 9 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- Last stable release on August 15, 2026
- 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.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

Behavior2/5
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 only states that it returns history, with no mention of side effects, error behavior, auth requirements, or what happens if no history exists. The description does not add meaningful behavioral context beyond what is obvious from the name.
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 a single, well-structured sentence with no filler. It front-loads the action and resource, achieving maximum clarity in minimal words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter and no output schema, so the description should clarify the return structure and any limitations. It does mention '(timestamp, price)' as the history format, but it omits details like time range, ordering, or pagination. For a no-output-schema tool, this is only marginally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does little to compensate. It says 'for a watch' but does not explain the watch_id parameter's semantics, source, or constraints beyond what the schema name implies. The parameter is a simple integer, but the description still adds minimal value.
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 uses a specific verb 'Return' and clearly identifies the resource as 'recorded price history (timestamp, price) for a watch.' This distinguishes it from siblings like track_price (which starts tracking) and list_price_watches (which lists watched items), leaving no ambiguity about its function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention exclusions or alternative tools, leaving the agent to infer usage solely from the tool name and general context. This is a clear gap for a tool in a set with related price-tracking operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It indicates a read-only list operation but does not explicitly state that it does not modify data, nor does it mention pagination, sorting, or what happens if no watches exist. The phrase 'list all' implies read-only, but the description lacks explicit behavioral context.
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 a single sentence that states the action, resource, and a key detail (most recent price). It is front-loaded and contains no fluff or repetition, earning a top score for conciseness.
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 the tool's simplicity (no parameters, no output schema), the description adequately covers its function. It specifies what is returned (watches with most recent price). However, a short clarification about the output being a list and the absence of filters would improve completeness, especially since sibling tools like get_price_history might cause confusion.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema coverage is 100% (vacuously). The description does not need to elaborate on parameter semantics, as the schema already defines an empty parameter set. The baseline of 4 for zero-parameter tools applies.
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 tool lists all saved price watches and includes their most recent recorded price. This distinguishes it from sibling tools like track_price (creates watches) and get_price_history (retrieves history for a specific watch), as it operates on the collection of watches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no explicit guidance on when to use this tool versus alternatives. It simply states what it does, leaving the agent to infer that this is for viewing all saved watches. There is no mention of exclusions, scenarios, or alternatives like get_price_history for individual watch details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the main side effects ('Saves the search as a watch and records its current lowest price') and the alert mechanism. However, it omits details like what happens if a watch already exists, return value, or permission requirements, which prevents a higher score.
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 three sentences, front-loaded with the core purpose, and every sentence adds value. It avoids fluff and clearly separates the primary action, follow-up action, and parameter explanations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (11 parameters, no annotations, no output schema), the description provides the core workflow but lacks comprehensive details about parameter interplay, return values, and edge cases. It is adequate for basic understanding but not fully complete for a tool with this many parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains only two parameters (target_price and label) out of 11. The remaining parameters (departure_id, arrival_id, outbound_date, return_date, etc.) rely on their names and defaults, but the description adds no meaningful context for them, leaving significant gaps.
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 starts with an explicit verb+resource: 'Start tracking the price of a route over time.' It clearly distinguishes this from siblings like search_flights (searching) and check_prices (checking existing watches) by emphasizing the action of creating a persistent watch and recording the current lowest price.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear workflow guidance by stating 'Re-check with check_prices (or the scheduled checker)' and explains the purpose of target_price and label. It does not explicitly state when not to use this tool, but the alternative is named and the intended lifecycle is clear, so it earns a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the burden. It discloses the tool's role in a workflow and the token linkage, but does not mention potential side effects (e.g., whether selecting a flight locks anything), error cases, or any read-only guarantee. While the tool appears read-only, the description does not explicitly state safety or lack of mutations.
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 concise and well-structured, with three sentences that first state the main action, then explain the token usage, and finally link to the output's usability. There is no filler or redundant information, making it efficient for an agent to scan.
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?
For a tool with two parameters and no output schema, the description integrates the tool into the broader journey (search_flights → get_return_flights → get_booking_options). It explains what the return options contain (booking_token) and how to use them. Missing details like max_results semantics or error handling, but these are minor for the overall workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It thoroughly explains departure_token (source and purpose), but does not mention max_results at all. The schema shows max_results has a default and is optional, but the description adds no insight into its behavior or acceptable values. Partial compensation.
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 tool's function as 'List the next set of options after selecting a flight,' specifying the verb (list), resource (options), and context (after flight selection). It distinguishes itself from siblings by explaining the token flow from search_flights and search_multi_city, and how booking_token connects to get_booking_options.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance: use it with a departure_token from a round-trip search_flights result to see return options, or from a multi-city result to advance to the next leg. It also notes that the returned booking_token is usable with get_booking_options, indicating follow-up actions. It doesn't explicitly state when not to use it, but the contextual instructions are strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It explicitly states that the price watch is deleted 'and its recorded history', which is a meaningful side effect beyond a simple delete. This adequately communicates the destructive nature and cascading effect.
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 a single, concise sentence that conveys the entire operation without any filler or redundancy. Every word earns its place, and it is front-loaded with the verb and object.
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?
For a low-complexity tool with one parameter and no output schema, the description is fairly complete. It states the action and the critical side effect (history deletion). It lacks details about return values or error cases, but these are less critical for a simple delete operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% coverage and only one parameter, watch_id. The description says 'by id', which matches the schema title 'Watch Id', but does not add significant extra meaning beyond the schema. It is adequate but minimal, not fully compensating for the low schema coverage.
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 uses a specific verb 'Delete' with a clear resource 'price watch' and adds the detail '(and its recorded history)'. This clearly distinguishes it from siblings like track_price, list_price_watches, and get_price_history. The operation is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the use case (when you no longer want to track a price), but it does not explicitly state when to use it versus alternatives or provide exclusions. The sibling tools provide context, but the description itself lacks explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It highlights that each watch incurs an external SerpAPI search, which implies cost/rate limits, and it discloses the side effect of recording prices. It doesn't cover error handling or auth, but for a simple tool this is solid.
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 two sentences: the first packs action and expected output, the second adds the critical operational cost (SerpAPI search count). Every word earns its place, with no redundancy or fluff.
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?
Although there is no output schema, the description explicitly states what the tool reports (drops or target hits) and its side effect (recording prices). It lacks details about empty watch lists or failure behavior, but given the zero-parameter simplicity, it is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
This tool has zero parameters, so there are no parameter semantics to clarify. According to the calibration baseline, 0 params earns a 4, and the description's mention of 'every price watch' implicitly explains the scope without needing parameter detail.
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 specifies the verb 'Re-check' and the resource 'every price watch', and it states concrete outcomes: record prices and report drops or target hits. This distinguishes it from siblings like list_price_watches (listing) and track_price (adding a watch), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'now' implies an on-demand refresh, and the note about using one SerpAPI search per watch signals cost considerations. However, it does not explicitly compare against alternatives or state when not to use it, leaving usage guidance 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the chaining behavior (departure_token to get_return_flights) and gives an example, but it omits return format, pagination, and read-only nature. This provides some transparency but not full coverage.
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 well-structured with a clear one-line purpose, a focused Args section, and a concrete example. The cross-reference keeps it concise while ensuring the key parameter is thoroughly explained.
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?
For a multi-leg search with chaining, it covers essential context: legs structure, shared parameters, and next-step token usage. It doesn't describe the output format, but given the tool's complexity and lack of output schema, it is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description compensates by giving a detailed, illustrated definition for the necessary 'legs' parameter. The reference to search_flights for the remaining ten shared parameters is a meaningful cross-reference, though not fully self-contained.
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 uses a specific verb 'Search' with a clear resource 'multi-city Google Flights itinerary (2+ separate legs)', which distinguishes it from single-flight search tools like search_flights. The explicit '2+ separate legs' scopes the tool precisely.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It clearly indicates multi-leg itineraries are the target and provides a chaining hint to get_return_flights via departure_token. However, it doesn't explicitly state when to prefer this over search_flights or list exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 the read-like nature ('List') and the source of the required token, but it does not mention return format, potential errors, or side effects. Given the simplicity of the operation, this is adequate but not detailed.
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 composed of two concise, front-loaded sentences. The first sentence states the primary purpose; the second provides a necessary usage instruction. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With only one parameter, no annotations, and no output schema, the description adequately covers the essential aspects: what it does, what input is needed, and where that input comes from. It is complete for a tool of this simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description explicitly explains that booking_token comes from search_flights or get_return_flights results, adding meaning beyond the schema's type/name. This compensates well for the lack of schema-level 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 uses a specific verb ('List') and clearly identifies the resource ('where to book a selected flight and the price at each provider'). It distinguishes itself from siblings like search_flights by focusing on the booking step, making the purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states the prerequisite: pass a booking_token from search_flights (one-way) or get_return_flights. This gives clear context on when to use the tool. It does not explicitly name alternatives or exclusions, but the instruction is specific enough to guide correct usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and adds valuable behavioral context: deep_search is slower but more precise, max_results caps options per bucket, and round-trip prices are the total fare. These details go beyond the schema and help set expectations for the agent.
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 structured as a clean Args list with terse, informative lines, and the closing note about round-trip fare/token adds high-value context without redundancy. For a tool with 15 parameters, the length is justified and every sentence earns its place.
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 the 15 parameters and lack of output schema, the description covers parameter formats, conditional requirements, and key behaviors like the round-trip token flow. It could be slightly more complete by describing the response envelope or error behavior, but it provides enough for an agent to select and invoke the tool successfully.
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?
The schema has 0% description coverage, so the description fully compensates by providing IATA code examples, comma-separated multiple origins, date formats, ISO 4217 currency, per-bucket max_results, and the deep_search tradeoff. Each parameter receives meaningful elaboration that turns the raw schema into actionable guidance.
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 uses the specific verb 'Search', identifies the resource 'Google Flights (via SerpAPI)', and scopes it to 'between two airports', which cleanly distinguishes from multi-city search and other flight tools. The mention of departure_token passing to get_return_flights further clarifies its role in the search workflow.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly guides round-trip usage by noting that the departure_token can be passed to get_return_flights for return options, and clarifies when return_date is required vs. ignored. It does not explicitly contrast with search_multi_city or track_price, but the 'between two airports' scope and token handoff provide strong contextual guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![claude-google-flights-mcp MCP server](https://glama.ai/mcp/servers/pisanuw/claude-google-flights-mcp/badges/card.svg)](https://glama.ai/mcp/servers/pisanuw/claude-google-flights-mcp)

Score Badge

Copy to your README.md:

[![claude-google-flights-mcp MCP server](https://glama.ai/mcp/servers/pisanuw/claude-google-flights-mcp/badges/score.svg)](https://glama.ai/mcp/servers/pisanuw/claude-google-flights-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/pisanuw/claude-google-flights-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server