Agentic Travel Recommendations API
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool serves a distinct purpose: list_members discovers valid IDs, get_member_profile provides detailed member context, and get_recommendations returns the actual recommendations. There is no overlap or ambiguity between them.
Naming Consistency5/5All tool names follow a consistent verb_noun snake_case pattern: list_members, get_member_profile, get_recommendations. The use of 'list' for collection retrieval and 'get' for single-item retrieval is a standard and predictable convention.
Tool Count5/5With exactly 3 tools, the server is minimal but well-scoped. Each tool is necessary and supports the core workflow of discovering members, understanding their profile, and generating recommendations. This fits comfortably within the ideal 3-15 range.
Completeness5/5The tool surface fully covers the recommendations domain: listing all members, fetching member profiles, and generating personalized recommendations. There are no obvious dead ends or missing operations for the stated purpose.
Average 4.2/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 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?
With no annotations provided, the description must carry the full burden of behavioral disclosure. The verb 'Retrieves' implies a read-only operation, and the description lists the returned data, but it does not explicitly state side effects, error behavior (e.g., member not found), or any rate limits or authorization requirements. This is acceptable for a simple lookup but not fully transparent.
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 two sentences: the first fronts the core action and data scope, the second the usage context. It is concise, free of filler, and every word earns its place.
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?
The tool is simple—one parameter, no output schema, no annotations. The description adequately covers the purpose, the returned data fields, and the canonical use case. For such a low-complexity tool, this is complete enough for an AI agent to select and invoke correctly.
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 provides 100% description coverage for the sole parameter, memberId, including its type and an example format. The description adds no additional parameter-specific semantics, so it does not go beyond what the schema already provides. The score is at the baseline of 3.
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 clearly states the tool's function using the verb 'Retrieves' and specifies the resource ('a member profile') and key data fields (loyalty tier, partner, travel history). It strongly differentiates from siblings: get_recommendations focuses on recommendations, and list_members lists members, while this tool retrieves a single member's profile.
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 explicitly recommends when to use the tool: 'Use this to understand who the member is before making recommendations.' This provides clear contextual guidance. It does not, however, explicitly mention when not to use it or name alternative tools, leaving room for a more thorough comparison.
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 disclosing behavior. It reveals that recommendations are filtered by partner rules including category exclusions and recommendation caps, and implies a dependency on get_member_profile. This adds meaningful context beyond the name, though it doesn't cover failure modes or exact return structure.
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 two sentences: the first states purpose and filters, the second gives a clear prerequisite. Both sentences are informative and free of fluff, making it maximally concise and well-structured.
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?
For a simple one-parameter read-only tool, the description covers the core behavior (returns recommendations), key filtering details, and a required dependency. It doesn't describe the response format or error conditions, but given the absence of an output schema and the tool's simplicity, it's reasonably complete.
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 already fully describes the memberId parameter (100% coverage). The description adds no additional parameter-specific details, so it neither enhances nor detracts from the schema's clarity. 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 clearly states the tool returns personalized travel recommendations for a member, with the specific verb 'returns' and resource 'travel recommendations'. It also mentions filtering by partner rules, which distinguishes it from sibling tools like get_member_profile and list_members.
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 explicitly instructs to call get_member_profile first, providing a clear prerequisite and ordering. However, it does not explicitly mention when not to use this tool or alternatives, though the clear resource distinction and naming make the usage context strong.
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?
There are no annotations, so the description must fully disclose behavior. It states the tool returns all available members, implying a read-only operation with no side effects. Yet it does not mention response shape, pagination, or any access constraints, leaving some behavioral ambiguity.
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 two concise sentences that are front-loaded—the first sentence states the core action, the second gives an explicit use case. No unnecessary words or repetition.
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?
For a tool with no parameters and no output schema, this description covers the core functionality and a known use case. It could expand on the return format, but the simplicity of the tool makes this adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema is empty (zero parameters) with 100% schema coverage. The description adds nothing about parameters, but since none exist, it is sufficient. Baseline score of 4 for zero parameters 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 clearly states the tool 'Returns all available members in the system,' which is a specific action and resource. It distinguishes from sibling tools like get_member_profile and get_recommendations by focusing on the full member list rather than individual profiles or recommendations. The added note about discovering valid member IDs reinforces the tool's purpose.
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 phrase 'Use this to discover valid member IDs' gives a clear context for when to call this tool. However, it does not explicitly contrast with sibling tools or state when not to use it, such as 'if you need a single member's details, use get_member_profile instead.'
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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