google-flights-proto-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation3/5
build_protobuf_search_url is clearly distinct as a URL builder, but discover_and_rank_complete and search_and_verify_top overlap heavily since both perform the same core search and ranking flow, differing only in the verification step. The descriptions help, but an agent could easily pick the wrong one when it only needs unverified results.
Naming Consistency4/5All tool names use snake_case and begin with a verb, which is a solid pattern. The minor inconsistency is that build_protobuf_search_url uses a simple verb_noun structure while the other two use verb_and_verb_complement, making the set slightly uneven but still readable.
Tool Count4/5Three tools is a reasonable size for a focused flight-search server that exposes a small pipeline: build URL, run search and ranking, then optionally verify. Each tool has a real purpose, though the set is slightly thin if broader flight-search operations were intended.
Completeness4/5The tools cover the main workflow from URL construction through ranking and verification, so most end-to-end flight search needs are addressed. Minor gaps exist such as no tool to view raw intermediate protobuf/HTTP responses, but agents can work around this by chaining the available tools.
Average 3.5/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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?
Annotations already declare readOnlyHint=true, and the description adds the protobuf+HTTP transport and the top-1-3 return behavior, which is modest extra context. It does not explain ranking criteria, external-call semantics, or failure behavior, but the read-only annotation lowers the burden.
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 a single efficient sentence that front-loads the core action and result. It contains no filler, though 'Use protobuf+HTTP' is terse and could be more descriptive without becoming bloated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity—a large nested request, an output schema, and two sibling tools—the description omits important context about what 'complete itineraries' means, how ranking works, and when to select this over the siblings. The output schema covers return structure, but behavioral and selection context are lacking.
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 coverage is 100%, with the nested request object documented as 'One Google Flights search and the controls used by the ranking pipeline.' The description adds no parameter-level detail, so it does not exceed the baseline established by the rich schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a concrete outcome ('pair full itineraries, rank them, and return top 1-3') and hints at a distinct mechanism ('protobuf+HTTP'), which helps separate it from the URL-building and verification siblings. However, 'pair' is jargon and not explained, so it stops short of a fully self-contained purpose statement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus build_protobuf_search_url or search_and_verify_top, and no mention of prerequisites or exclusions. The word 'Use' implies a general directive, but the description does not provide selection conditions or alternatives.
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?
Annotations already declare readOnlyHint=true, so the read-only safety profile is established. The description adds the pipeline stages and the notable Playwright verification step, but it does not explain what verification entails, potential latency, or whether browser automation could have observable side effects. With annotations covering the main safety burden, this is adequate but not rich.
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 extremely compact, with no filler words, and the arrows convey a clear execution order. It is front-loaded and efficient, though the internal jargon makes it less immediately readable than a plain-language sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the request object and the existence of an output schema, the description provides only a pipeline synopsis. It omits when to call the tool, what 'verification' actually validates, and how results are returned. Annotations and schema fill some gaps, but the description is incomplete for a tool this rich.
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%, and the request object is described as a Google Flights search with ranking controls. The tool description adds no parameter-level meaning beyond the schema; 'top 1-3' maps loosely to top_n, but the schema already documents that. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description names a concrete pipeline—protobuf to HTTP to pairing/ranking to top 1-3 to Playwright verification—and the title clarifies the resource: top itineraries. It is specific enough to distinguish this from the siblings at a high level, though it does not explicitly contrast with discover_and_rank_complete or state the final return value.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit statement about when to use this tool versus build_protobuf_search_url or discover_and_rank_complete. The 'top 1-3' mention weakly implies small result sets, but no when/when-not conditions or alternative routing are provided.
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?
The readOnlyHint annotation already indicates no side effects, and the description reinforces this with 'without making a network request.' It adds useful behavioral context by clarifying that the tool performs local construction only, which is meaningful beyond the annotation alone.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence with no filler. It front-loads the core action and resource, then adds the most important behavioral qualifier: no network request.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists and the input schema fully documents the request object, the description only needs to convey the tool's high-level purpose and side-effect profile. 'Build a real tfs protobuf search URL without making a network request' does exactly that, leaving no significant gap.
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?
The input schema has 100% description coverage, including a well-described nested request object with defaults, ranges, and field-level descriptions. The description itself adds no parameter-level meaning, so the baseline of 3 is appropriate.
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 uses a specific verb ('Build') and resource ('tfs protobuf search URL') and adds the critical distinction that it does not make a network request. This clearly differentiates the tool from siblings such as discover_and_rank_complete and search_and_verify_top, which imply actual searching and verification.
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 'without making a network request' clause signals that this tool is for pure URL construction rather than executing a flight search, which gives an agent clear situational context. It does not explicitly name alternative tools, but the distinction from the sibling tools is strongly implied.
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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