Canada Travel Advisory MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a distinct purpose: single-country lookup, list/filter across all, recent changes, search/disambiguation, comparison, and statistics. No two tools overlap in function; an agent can clearly choose based on need.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (get_, list_, get_recent_, search_, compare_, get_). This predictability helps an agent understand the action and target without ambiguity.
Tool Count5/5
Six tools is ideal for the scope: covering single query, list, recent updates, search, comparison, and statistics. Each tool serves a distinct and necessary use case without redundancy or unnecessary bloat.
Completeness5/5
The tool surface covers all common travel advisory workflows: lookup, filtering, discovering recent changes, searching/disambiguation, multi-country comparison, and aggregate statistics. No obvious gaps—users can answer questions about specific countries, broad trends, or comparisons.
Average 3.9/5 across 6 of 6 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
- 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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It only mentions that the output is 'ranked from highest to lowest risk', but does not disclose whether the operation is read-only, any authentication requirements, rate limits, or likely output structure. This is minimal behavioral transparency.
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 long, front-loaded with the primary action, and every word adds value. No fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and annotations, the description is insufficient. It does not explain what the output looks like (e.g., list of advisory objects, risk levels, country names), nor does it mention error handling, limits, or performance considerations. For a comparison tool, the output format is critical context.
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 100% with both parameters already described. The description adds no additional meaning to the parameters beyond stating that the tool accepts 'several destinations' (countries parameter) and that results are ranked. This is a baseline score given the schema already does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: comparing travel advisories for multiple destinations and ranking them by risk. It distinguishes from siblings like get_travel_advisory (single country) and list_travel_advisories (all advisories, not sorted). However, it does not explicitly call out these alternatives.
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 provides a usage hint ('Useful for planning a multi-country trip') but does not explicitly state when to use this tool instead of alternatives, nor does it mention any exclusion criteria or prerequisites.
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?
With no annotations, the description bears full burden for behavioral disclosure. It only describes the output contents, not side effects, performance, or operational constraints such as read-only nature or data freshness.
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 a colon-led list. It is front-loaded with the overall purpose and every clause adds value without redundancy.
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 one optional parameter and no output schema, the description covers the key output elements adequately. It could mention data freshness or caching, but is sufficient for an agent to understand what statistics are returned.
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 100% and the description adds no extra meaning to the single 'language' parameter. Baseline score of 3 is appropriate as the schema already fully describes it.
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 provides an overview of the advisory dataset with specific metrics (risk levels, regional advisories, change types, timestamp). This distinguishes it from sibling tools that handle individual advisory retrieval, listing, or searching.
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 usage for obtaining aggregate statistics but does not explicitly state when to use it over alternatives like get_travel_advisory or list_travel_advisories. No when-not-to-use or prerequisites are mentioned.
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 bears full responsibility for behavioral disclosure. It only states the search criteria (partial name, ISO code, slug) but does not mention whether results are approximate, case‑sensitive, or what happens when no match is found. The return format (e.g., list of countries with identifiers) is also omitted.
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?
Two sentences, no wasted words. The first sentence states the core action, and the second immediately provides usage context. Perfectly front‑loaded and efficient.
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 description covers purpose and usage context well, but with no output schema, it should describe what the tool returns (e.g., a list of countries with names, codes, slugs). It also lacks details about behavior like default limit, language effect on output, or result field semantics.
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 100%, so the baseline is 3. The description confirms that the query parameter accepts partial names or codes and gives examples in the schema, but it adds little extra semantics beyond what the schema already documents.
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 searches destinations by partial name, ISO code, or slug, and explicitly positions it as a discovery/disambiguation step before calling get_travel_advisory. This distinguishes it from sibling tools and provides a specific verb+resource.
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 advises using this tool to discover or disambiguate a destination before calling get_travel_advisory. This is strong contextual guidance, though it does not mention when not to use it or alternative tools like list_travel_advisories.
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 provided, the description carries the full burden of behavioral disclosure. It explains the scope (all 230 countries), sorting behavior ('risk' and 'updated' are descending), and the risk_level parameter's semantics with labels. However, it does not mention rate limits, authentication needs, or whether this operation is read-only (which is reasonable given the apparent read nature, but transparency could be higher).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise at three sentences, each providing useful information: purpose, example usage, and sorting options. The structure flows logically from what the tool does, to how to use it, to sorting options. However, the sorting example could be integrated into the purpose to reduce redundancy.
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 has 7 parameters with 100% schema coverage but no output schema, the description provides enough context for an AI agent to understand what it does and how to use it. The naming and example usage help the agent select it over siblings. However, it does not describe the output format, which could be critical for proper usage.
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 description coverage is 100%, so the schema already documents all 7 parameters in detail. The description adds value by explicitly explaining the behavior of sort (descending for 'risk' and 'updated'), providing a concrete example for min_risk_level (value 2), and clarifying risk_level's dual acceptance of numeric and label text. However, the description does not significantly enhance understanding beyond the schema.
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 and filters travel advisories across all 230 destinations, specifying the resource (travel advisories) and action (list and filter). It distinguishes itself from siblings like 'get_travel_advisory' (which likely returns a single advisory) and 'search_countries' (which likely returns countries, not advisories).
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 guidance on when to use this tool, including a concrete example ('Use min_risk_level: 2 to find everywhere Canada advises against travel'). It implies when to use alternatives like 'get_travel_advisory' for a single destination but does not explicitly state when not to use it or list alternative tools for specific purposes.
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?
Annotations are absent so description carries full burden. It describes the ouput (risk level, text, updates, link) and implies a read only operation. However, it does not mention errr handling or what happens if the country is not found, which slightly limits transparency.
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?
Two sentences, front loaded with purpose and input format, then output details. No verbose or redundant content.
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, description adequately lists output fields. It covers input variations and output, but lacks detail on default language behavior and error cases, leaving minor gaps.
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 100%, but the description adds value by explaining that country acccepts English/French names, ISO codes, and slugs. This reinforces and clarifies the schema description.
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 gets a single travel advisory from the Government of Canada. It specifies the verb 'get' and the resource 'travel advisory', and differentiates from siblings like list_travel_advisories which imply multiple advisories.
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?
While the description implies use for single countries, it does not explicitly state when to use this versus other tools like list_travel_advisories or get_recent_updates. No when not guidance is provided, making it reliant on agent inference.
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?
No annotations provided, so the description must do all the work. It clearly states the ordering (newest first), optional time window (days parameter), type restriction (update_type), and default behaviors. It doesn't explicitly state if this is a read-only operation, but the verb 'Get' strongly implies it, and nothing suggests side effects. A small gap is not mentioning pagination or what happens with empty results.
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?
Three sentences, zero fluff. The purpose is front-loaded in the first sentence, usage guidance in the second, and optional parameter usage in the third. Every sentence earns its place without repetition.
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?
Despite no output schema and moderate complexity (4 params, 2 enums), the description is fully self-contained. Agents can understand what the tool does, when to use it, and how to use the parameters. The absence of output schema is compensated by the clear behavior description (list of recent advisories).
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 already covers all 4 parameters with descriptions (100% coverage), so baseline is 3. The description adds value by explaining the purpose of the date filter and the update_type restriction with concrete examples like 'Regional advisory added', which goes beyond the schema's terse description. However, it doesn't add context for limit or language beyond what the schema provides.
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 explicitly states the tool gets the most recently updated advisories, ordered newest first. It clearly distinguishes itself from siblings like get_travel_advisory (single advisory) and list_travel_advisories (likely a listing without update recency) by focusing on recency and change tracking.
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 directly says 'Use this for "what changed this week" questions', providing a concrete usage scenario. It also explains optional restriction by update_type, giving agents clear guidance on when to apply that parameter.
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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