anz-schedule-brain
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool targets a distinct aspect of ANZ scheduling: public holidays, school terms, and business days. There is no overlap between checking a date's holiday status, determining term information, or calculating the next business day.
Naming Consistency4/5All tool names use lowercase snake_case with a verb prefix ('is_' for a boolean check, 'get_' for retrieval). The pattern is consistent and predictable, though the mix of 'is_' and 'get_' is a minor deviation from a uniform 'get_' style.
Tool Count5/5Three tools form a well-scoped set for a niche ANZ schedule domain. Each tool provides essential functionality without unnecessary bloat, fitting comfortably in the ideal 3-15 range.
Completeness5/5The tool set covers the core scheduling needs: public holiday lookup (including full region lists), school term status and schedules, and business day calculation. No obvious gaps exist for the stated purpose, as all responses include relevant structured data and summaries.
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
- 0 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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the result excludes public holidays and is Monday–Friday, and describes the return format (summary string + structured data object with status and skipped holidays). This goes beyond the schema and provides useful behavioral context, though it does not clarify whether the input date itself counts if it's a business day.
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, front-loaded with the primary purpose, and includes all essential information without redundancy. It lists the allowed regions briefly and describes the output, all without unnecessary fluff.
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 absence of an output schema, the description provides a reasonable overview of the return value (summary string and structured data object with status and skipped holidays). It is sufficient for a moderately simple tool, though it could detail the structured object's fields further, but it is not unnecessarily vague.
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 description coverage is 100%, so the baseline is 3. The description references the region codes and date format, but these are already fully covered in the schema descriptions. No additional meaning is added beyond what the schema provides.
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: given a date and ANZ region, return the next business day excluding public holidays. It uses a specific verb ('return') and identifies the resource ('next business day'), and distinguishes it from siblings like is_public_holiday by focusing on computing the next business day.
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 implies usage (when you need the next business day) but does not explicitly state when to use this tool versus alternatives such as is_public_holiday or get_school_term. No exclusions or alternative comparisons 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 transparency burden. It explicitly states that the tool returns a summary string and a structured object containing term details, week number, school days elapsed/remaining, and the full 2026–2027 schedule. This clarifies the read-only nature and output composition, though it doesn't discuss edge cases or date-range limitations.
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 a single, information-dense sentence that front-loads the core purpose and then concisely lists the returned data. It avoids unnecessary filler while providing essential details. Slightly dense but efficient.
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 only two parameters and no output schema, the description sufficiently explains the return payload and region options. It mentions the full schedule range, which is useful context. It doesn't explain error conditions, but for the tool's simplicity, the description is largely 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 schema description coverage is 100%, so the schema already documents both parameters (date and region). The description adds only minor context (e.g., 'ANZ region' and the region codes), but nothing fundamentally beyond the schema. Baseline 3 applies because the description doesn't need to compensate.
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 uses a specific verb ('Determine whether') and a clear resource ('falls within a school term'), while enumerating all valid ANZ region codes. It distinguishes itself from siblings like is_public_holiday or get_next_business_day by focusing solely on school-term membership.
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 clearly establishes the context for use (given a date and region) and implies its utility for school-term-related queries. It doesn't explicitly name alternatives or state when not to use it, but the covered regions and return details give sufficient context without exclusions.
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 discloses that the tool returns a summary string and a structured data object with the holiday name, type, and a chronological list of 2026–2027 holidays. However, it does not elaborate on error handling or edge cases (e.g., invalid dates or dates outside the listed range), so it is 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, front-loaded with the primary purpose and followed by a concise explanation of the return value. No unnecessary information is included, and each sentence 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?
Given that there is no output schema, the description sufficiently explains the return value (summary string and structured data object with holiday name, type, and holiday list) and the scope of the tool (regions and date check). It is complete enough for an agent to correctly select and invoke the 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?
Both parameters are fully described in the input schema with clear descriptions and an enum for the region. The tool description adds no additional semantic information beyond what the schema already provides (e.g., it repeats the region list), so the 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 uses a specific verb ('Check whether') and resource ('a given date is a public holiday') and explicitly lists all supported regions, distinguishing it from sibling tools like get_school_term and get_next_business_day. It clearly identifies the tool's unique function.
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 provides clear context for when to use the tool (checking if a specific date is a public holiday in an ANZ region) but does not explicitly name alternatives or exclusion conditions. The purpose is clear enough to infer usage, but it stops short of explicit when-not guidance.
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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