cityparity

Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool has a clearly distinct purpose: compare_cities for head-to-head, get_city_summary for single city profile, get_inbound_tax_regime for tax schemes, get_safety_net for batch safety net, list_cities for discovering slugs, and rank_cities for ranking. No overlapping functionality.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (e.g., compare_cities, get_city_summary, list_cities). No mixing of conventions.
Tool Count5/5
With 6 tools, the set is well-scoped for a city comparison service. Each tool covers a necessary operation (list, single summary, compare, rank, specific lookups) without being too few or excessive.
Completeness5/5
The tool surface covers the full workflow: discovering cities, getting single city profiles, comparing two cities, ranking many, and looking up specific tax regimes or safety nets. No obvious gaps for the stated purpose.
Average 4.6/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 8 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
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?
Annotations already indicate read-only, non-destructive, and idempotent behavior. The description reinforces this by stating 'Read-only, no side effects' and adds that it returns both a text summary and structured JSON. This adds value beyond annotations.
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 concise sentences: first states purpose, second provides usage guidance and behavioral note. Front-loaded with key information, no wasted words.
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 simple tool (one optional parameter) and rich annotations, the description covers the essential aspects. It mentions what to call first and the return format, though it doesn't detail the structured JSON format. Sibling tools are listed but not contrasted, but the purpose is sufficiently clear.
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 input schema already provides a detailed description of the 'country' parameter (case-insensitive, canonical names, examples). The tool description merely paraphrases this as 'optional country filter narrows to one country,' adding minimal additional meaning. With 100% schema coverage, baseline is 3.
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 all city slugs grouped by country, and that it should be called first to discover valid slugs. This explicitly identifies the verb, resource, and context, and distinguishes it from sibling tools that perform different tasks like comparing or summarizing cities.
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 directs the agent to call this tool first to discover valid slugs before using other tools. It also mentions the optional country filter. While it does not explicitly state when not to use it, the context is clear that it is the discovery entry point.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, destructiveHint, idempotentHint; the description adds that it is read-only with no side effects and returns both text and structured JSON. This fully discloses behavior without contradicting annotations.
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 but informative, front-loading the purpose and listing modeled regimes. Every sentence adds value, though a minor reduction in length could improve conciseness. Structurally 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?
Given the output schema exists, the description need not detail return values beyond mentioning text summary and structured JSON. It adequately covers the tool's scope, null behavior, and examples, leaving no major gaps.
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 input schema has 100% description coverage for the city parameter, including guidance to use list_cities and mention of null returns. The description does not add significant new parameter-level information beyond the schema, meeting the baseline for high coverage.
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 looks up a city's inbound-worker tax regime, a specific and distinct function from general cost-of-living tools. It lists modeled regimes, distinguishing it from sibling tools like get_city_summary or compare_cities.
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 indicates most cities return null and lists specific modeled regimes, providing clear context for when the tool is applicable. However, it does not explicitly contrast with sibling tools like get_city_summary, so guidance on alternatives is slightly lacking.
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 already provide readOnlyHint, destructiveHint false, idempotentHint true. The description reinforces 'Read-only, no side effects' and adds behavioral details like data freshness and the return type (text summary + structured JSON). This adds value beyond annotations.
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 three sentences, each earning its place: first states purpose and content, second gives usage guidance, third declares side effects and output format. No wasted words, well-structured.
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?
Given the tool's complexity (single parameter, output schema present), the description covers all necessary context: input, output format (text + JSON), read-only nature, and data freshness. Output schema handles return details, so description is fully adequate.
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 description does not add meaning beyond the input schema for the single 'city' parameter. Schema coverage is 100% and includes its own description. The description mentions 'slug' implicitly but does not elaborate further. Baseline of 3 applies.
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 a 'concise profile of one city' with specific categories (currency, tax, costs, safety-net), and distinguishes itself from the sibling 'compare_cities' by noting it is 'lighter' and for single-place queries.
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 explicitly guides usage: 'use when the user is asking about one place rather than a comparison.' It also contrasts with 'compare_cities' as a lighter alternative, giving clear context for selection.
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 already declare read-only and idempotent. The description adds that there are no side effects and that the return includes a text summary plus structured JSON. This is consistent and adds value beyond the annotations.
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 paragraph. Every sentence earns its place, starting with the core purpose and then differentiating from siblings.
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?
Given the low complexity (1 param, good schema, annotations, output schema exists), the description covers all necessary aspects: what it does, how many items, what metrics, and how it relates to other tools.
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?
The only parameter 'cities' already has a good schema description. The description adds context that it's a batch operation and mentions the dimension score. It does not repeat the schema but complements it well.
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 performs a batch lookup of safety-net values (parental leave, healthcare, vacation, holidays) plus a dimension score for 1-20 cities. It distinguishes itself from siblings by specifying alternatives for full profiles and two-city comparisons.
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?
Explicitly states when to use (batch lookup for multiple cities) and when not to use (for full profile or two-city comparison). Names specific alternatives: get_city_summary and compare_cities.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. Description reinforces 'Read-only, no side effects' and adds RSU treatment detail, providing context beyond annotations without contradiction.
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 paragraphs, front-loaded with key outputs, every sentence adds value. No wasted words; structure is logical and easy to scan.
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?
Given the tool's complexity (7 parameters, nested objects) and that output schema exists, the description covers purpose, usage, limitations, and output format completely. No 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%, so schema already documents parameters. The description adds value by clarifying that RSU income is not a parameter, explaining the rationale. This goes beyond the baseline of 3.
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 compares two cities for a household, listing specific outputs like take-home pay, cost breakdown, target salary, lifestyle deltas, and a quality score. It distinguishes from siblings by naming get_city_summary and rank_cities.
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?
Explicitly says 'Use this for a head-to-head between two named cities' and contrasts with alternatives for single city or ranking many cities. Also clarifies RSU is not a parameter because it's source-only.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that it is 'Read-only, no side effects; returns a text summary plus structured JSON.' This aligns with annotations and adds context about output format.
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 paragraph. Every sentence provides essential information without redundancy. It is front-loaded with the core action and then details filters and usage.
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?
Given the tool's complexity (4 parameters, nested objects, no required fields), the description covers all aspects: purpose, usage, behavioral traits, parameter details, and output. The presence of an output schema further supports completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description elaborates on each parameter: it mentions default weights (30,20,15,15,20), explains that filter combinations use intersection, and specifies defaults for scenario fields (e.g., has_partner default false, gross_salary_usd default 100000). This adds significant value 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 uses specific verbs ('rank') and clearly identifies the resource ('cities', 'composite quality-of-life score'). It distinguishes from the sibling `compare_cities` tool by explicitly stating its use for shortlisting across many cities vs. head-to-head comparison.
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 provides explicit guidance: 'Use this to shortlist across many cities; for a head-to-head between two named cities use compare_cities.' It also clarifies that the same scenario is applied to every city, ensuring comparability.
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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