aria-mcp-affald-horsens
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clear, distinct purpose: address resolution, next collection, and calendar of upcoming collections. No overlap.
Naming Consistency5/5All tool names use consistent snake_case verb_noun pattern: find_address, next_collection, collection_calendar.
Tool Count5/5Three tools are well-scoped for the domain of waste collection lookup in a single municipality—sufficient without being excessive.
Completeness5/5The set covers address disambiguation, immediate next collection, and future planning via calendar. No obvious gaps.
Average 4.3/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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.
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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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It explains the 'putOutTonight' flag and its use for reminders, and clarifies address handling (free text or ID, ambiguity resolution). No destructive behavior is described, which is appropriate for a read-only tool.
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 concise, covering key information in a few sentences without unnecessary verbosity. It could be improved with bullet points, but remains clear and front-loaded.
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?
Given the simple tool (one parameter, no output schema), the description is sufficiently complete. It explains the return value (next collection date per fraction, with flag), address format, and ambiguity handling. No major gaps.
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?
Schema coverage is 100% but the description adds value by explaining that 'address' can be a free-text string or a PerfectWaste addressID, and that ambiguous queries prefer 8700 Horsens. This goes beyond the schema's basic type description.
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 explicitly states 'Return the next upcoming waste collection date for each fraction at a given Horsens address.' It lists specific fractions and the 'putOutTonight' flag, clearly differentiating from sibling tools like 'collection_calendar' and 'find_address'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions that ambiguous queries prefer 8700 Horsens, but does not explicitly state when to use this tool over siblings like 'collection_calendar' or 'find_address'. The usage context is implied but not directly compared.
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 describes return fields (date, label, daysUntil, fractions) but does not disclose error handling, required permissions, or behavior on invalid addresses. Lacks details on side effects or rate limits, though tool is read-only.
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?
Two well-structured sentences: first states core function and return format, second gives usage guidance, third details parameters. No fluff, each sentence adds value.
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?
Covers purpose, usage, parameters, and return format (important since no output schema). Missing details on authentication, error handling, or scope limitations beyond 'Horsens address'. Adequate for a simple read tool.
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 descriptions identical to the description. The description restates parameter meanings (address type, weeks range) without adding new semantic value beyond the schema, so 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 upcoming waste collection events for a Horsens address over N weeks, using specific verb 'return' and resource 'waste collection events'. It distinguishes from siblings find_address (address lookup) and next_collection (single next event) by emphasizing multi-week look-ahead.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: 'Use this when ARIA needs to plan ahead or answer "hvornår tømmes X de næste uger?"' This directly guides the agent to choose this tool for planning multiple weeks rather than a single next collection.
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, but description discloses return format, limit of 30 candidates, and relevance ordering. Does not mention side effects or rate limits, but acceptable for a search tool.
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?
Two sentences with no wasted words; first sentence states purpose, second gives usage guidance and example. Very efficient.
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?
For a single-parameter search tool with no output schema and two siblings, the description covers input, output, limits, ordering, and usage context. Completely sufficient.
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?
Schema has 100% coverage with a description for the query parameter. The tool description adds context about search scope (Horsens Kommune) and disambiguation, enhancing meaning beyond schema.
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 it searches for a Horsens Kommune address and returns candidates with addressID and displayName. It differentiates from siblings by specifying this tool resolves addresses before using next_collection or collection_calendar.
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?
Explicitly says to use before next_collection or collection_calendar, and to disambiguate multiple matches. Provides an example. Could mention when not to use, but context 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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