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kimhjort

aria-mcp-trafik-dk

by kimhjort
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TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation4/5

    Tools have overlapping purposes (events_near and traffic_events both return current events), but descriptions clarify differences: events_near is location-based, traffic_events is general. roadworks is distinct. Overall, agents likely select correctly with clear prompts.

    Naming Consistency2/5

    Tool names lack a consistent pattern: 'events_near' combines noun+preposition, 'roadworks' is a single noun, 'traffic_events' is noun_noun. No verb_noun or consistent convention, making predictions harder.

    Tool Count4/5

    Three tools is a reasonable count for querying Danish road events. It covers current incidents, planned roadworks, and proximity search without unnecessary duplication, though slightly minimalist.

    Completeness4/5

    Covers the main needs: current events globally and by location, and planned roadworks. Missing features like event details by ID or historical data, but sufficient for real-time trip planning.

  • Average 4.1/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 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 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.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Discloses key behavioral traits: returns only Point geometry events from Vejdirektoratet, excludes events without coordinates, and sorts by distance. However, it does not cover error conditions, rate limits, or any side effects. No annotations are present, so the description carries the full burden.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Description is three sentences plus an example, front-loaded with purpose. Every sentence adds value, with no superfluous text.

    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?

    Adequately covers purpose and constraints given the simple input schema, but lacks details on output format and error handling. With no output schema, the description could be more complete.

    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 clear descriptions for all three parameters. The description adds no extra meaning beyond the schema, only providing an overall example that uses the parameters.

    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?

    Describes specifically finding current traffic events by geographic radius, listing event types, and noting that only events with Point geometry are returned. Clearly distinguishes from sibling tools 'roadworks' and 'traffic_events' by focusing on location-based filtering.

    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?

    States it is useful for checking conditions near a destination or along a route, and provides a concrete example. However, it does not explicitly mention when not to use it or contrast with sibling tools beyond the implicit filtering.

    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 provided, so description carries full burden. It discloses that it fetches data from two feeds and returns specific fields, but does not mention side effects, rate limits, or authentication. It adds some actionable context but lacks depth.

    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 action, data sources, and return values. No wasted words. Efficient and clear.

    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 purpose, data sources, and return fields. It lacks details on filtering behavior, pagination, or errors, but is largely complete for the tool's simplicity.

    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 description coverage is 100%, so baseline is 3. The description does not add any meaning about the 'area' parameter beyond what the schema provides. No value added.

    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?

    Description clearly states the tool fetches planned and ongoing roadworks on Danish state roads from Vejdirektoratet, combining two specific feeds. It specifies the return fields, 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 Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Explicitly tells when to use: 'when ARIA needs to warn about scheduled disruptions before a drive.' No explicit when-not or comparison to siblings, but the specific use case is 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?

    With no annotations provided, the description carries the full burden. It discloses expected return fields (type, road, location, times, severity) and update behavior. This is sufficient for a read-only data fetch with no side effects.

    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: three sentences covering purpose, use case, and update frequency. No wasted words; every sentence serves a purpose.

    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 optional parameters and no output schema, the description adequately covers return fields and update frequency. It provides enough context for correct invocation without overspecifying.

    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% and both parameters have descriptions. The description adds value by explaining that the 'area' filter is case-insensitive and matches against multiple fields, and it elaborates on the enum values in plain language.

    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 it fetches Danish road traffic events from a specific source (Vejdirektoratet), lists event types (incidents, accidents, etc.), and distinguishes from siblings like 'events_near' and 'roadworks' by focusing on state roads.

    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 suggests a concrete use case: warn ARIA before Kim drives. It also notes data update frequency (every 3 minutes), aiding timely decisions. No explicit when-not guidance, but the purpose is 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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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