mcp-mayo
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation2/5
Several tool pairs have unclear boundaries, such as list_active_employees vs search_active_employees, get_salary_bonus_list vs get_monthly_payroll_report, and export_employees vs export_employees_full. While descriptions attempt to differentiate them, the overlap makes it easy for an agent to select the wrong tool.
Naming Consistency3/5Most tools follow a snake_case verb_noun pattern, but the verbs are inconsistent: list, get, search, and export are used interchangeably without a clear rule. For example, list_active_employees and search_active_employees both exist, and get_ vs export_ prefixes are used for similar data.
Tool Count2/5With 33 tools, the server is well beyond the typical 3-15 range. Although the HR domain is broad, many tools are redundant or could be consolidated, such as the multiple export variants and monthly insurance getters, making the count feel excessive.
Completeness4/5The server covers a wide range of HR data including employees, organization, attendance, leave, salary, insurance, and expatriation. Composite tools like get_employee_profile and get_attendance_summary fill gaps, though write operations are absent, which is acceptable for a reporting-focused server.
Average 3.7/5 across 33 of 33 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 1 community issues answered or closed in the last 6 months
- 1 commit in the last 12 weeks
- Last stable release on
- 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.
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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If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
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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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It does not explain that this is a read-only operation, what 'effective' means (e.g., as-of-date semantics), how large the response may be, whether pagination is involved, or any performance implications. It only vaguely mentions filters.
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 brief and to the point, using two sentences with no redundant filler. It front-loads the main purpose and mentions filters. However, the term 'PA' is unexplained and could cause confusion, slightly reducing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 8 optional parameters, no required parameters, no output schema, and no annotations, this description is underspecified. It does not explain return format, whether results are paginated, what 'effective' means, or how this tool relates to other export tools. The presence of many similar siblings increases the need for more contextual information.
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 descriptions cover all 8 parameters at 100%, so the baseline is 3. The description adds little beyond the schema: 'every PA field' implies the export scope but does not clarify parameter formats or usage. Since parameter descriptions are already detailed, the description does not need to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('Export') and resource ('effective employee data') with a scope ('every PA field'). While 'PA' is not explained, it distinguishes the tool's comprehensive nature from simpler export tools like export_employees. However, it does not explicitly differentiate it from all siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool compared to alternatives such as export_employees, list_active_employees, or export_organization_with_employees. 'Supports many optional filters' hints at flexibility but provides no exclusions or decision criteria.
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 carries the full burden. It adds only the qualifier 'effective' but does not disclose whether this is a read-only operation, the output format, pagination behavior, or any other side effects. This is insufficient for an export tool operating on master data.
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 one sentence with no fluff. 'Export effective organization master data with all fields' packs the key points; even 'OM endpoint' is a concise technical aside. Every word contributes to the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is too sparse given the complex sibling landscape. It fails to explain what 'organization master data' includes, how this differs from export_organization_with_employees or get_organization_snapshot_as_of, and what the 'OM endpoint' means. It also doesn't clarify the role of the language parameter.
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 single parameter 'language' is fully described in the schema ('Language code'), so schema coverage is 100%. The description adds no meaning beyond the schema, which matches the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Export') and the resource ('effective organization master data with all fields'). It distinguishes itself somewhat by noting 'effective' data and 'all fields', but it doesn't explicitly call out sibling tools like export_organization_with_employees or get_organization_tree, leaving some ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus the many related sibling tools. The description does not mention any alternatives, exclusions, or specific scenarios where this export is preferred. The 'OM endpoint' hint is technical and not meaningful for usage decisions.
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 exist, so the description carries the full transparency burden. It reveals the data scope ('effective') and component types ('OM + PA') but omits crucial behavior such as output format, pagination, permissions, or exclusion of historical data. The undefined abbreviation 'OM + PA' further reduces clarity.
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 a single, focused sentence with no filler, redundancies, or unnecessary context. It is efficiently front-loaded with the action and resource, making it exemplary for this tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, no annotations, and a description that fails to explain the return format or clarify how it differs from numerous sibling export tools. The lack of usage guidance and behavioral detail makes it incomplete for an agent navigating a complex tool ecosystem.
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 input schema fully documents the only parameter 'language' with a description and default value, achieving 100% schema description coverage. The description adds no parameter-level detail, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Export') and the specific resource ('effective organization units together with their employees'), with 'OM + PA' adding domain-specific depth beyond the tool name. However, it does not explicitly differentiate from sibling export tools like export_organization_full or export_employees_full.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to choose this tool over the many sibling export tools, nor does it mention any prerequisites or exclusions. The term 'effective' hints at a scope constraint but does not convey the intended use case.
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 must disclose behavior, but it only says 'Export reference code tables'. It does not specify output format, whether it triggers a download, what tables are included, or any side effects. Minimal transparency beyond the action itself.
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, direct sentence that is easy to parse and front-loaded with the key action and target. It is appropriately sized for a simple tool, though it omits details that might be expected in a fuller description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations, no output schema, and only one optional parameter, the description is thin. It leaves unanswered what 'reference code tables' actually contain, what the output looks like, and how the language choice impacts results, making the description insufficient for fully informed invocation.
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 input schema has one parameter, 'language', with a description ('Language code') and default 'zh-tw', providing 100% schema coverage. The description adds no additional meaning about how the language parameter affects the export, so it does not exceed the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Export') and resource ('reference code tables') used by the People+ (PA) module. It distinguishes itself from sibling export tools by scoping to PA reference tables, though 'reference code tables' remains somewhat vague about the actual data content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The many sibling export tools could overlap in purpose, but the description does not mention any exclusions or preferred contexts, leaving the agent to infer usage from the name alone.
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 present, so the description carries the full burden of behavioral disclosure. It only states the action and output type without detailing side effects, return format, or any operational constraints. There is no mention whether the export is immediate or asynchronous, or what the response looks like.
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 a single sentence of eight words, immediately front-loading the verb and object. There is no redundant or extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having a single optional parameter and no output schema, the description is too sparse to fully inform an agent. It does not explain the expected output format (e.g., file download vs JSON), the effect of the language parameter, or any necessary preconditions. Since there are no annotations to fill this gap, the tool description seems insufficient for robust invocation.
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 already describes the 'language' parameter as 'Language code' with a default of 'zh-tw' (100% coverage). The description does not add any additional context about how language affects the export, such as localization of field definitions, so it adheres to the baseline 3.
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 the specific verb 'Export' and identifies the resource 'custom field definitions' with the scope 'configured by the tenant', which clearly distinguishes it from sibling employee/attendance export tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternative export tools included in the sibling list. It does not mention any prerequisites, use cases, or when not to use it.
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 must carry the burden of behavioral disclosure. It mentions pagination, which is a useful behavioral trait, but it does not explicitly state whether the operation is read-only, what the output format is, or any side effects. This leaves significant behavioral uncertainty.
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 a single sentence that is front-loaded with the verb 'Export' and the resource, and it includes the key qualifiers 'within a date range' and 'paginated'. Every word adds value, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description should provide more context about the return value, the meaning of 'change orders', and how pagination is handled. The presence of many sibling export tools increases ambiguity, and the one-sentence description does not fully clarify when this tool is the right choice.
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 input schema provides full (100%) descriptions for all 5 parameters, including date formats and pagination defaults, so the schema does the heavy lifting. The description adds no additional parameter semantics beyond mentioning the date range and pagination, which are already covered in the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool exports organization change orders within a date range, giving a specific verb and resource. However, it does not explicitly differentiate from similar sibling tools like export_employee_changes or export_organization_full, so it doesn't fully achieve the highest clarity score.
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 the tool is for exporting organization change orders filtered by date range, but it does not state when to prefer this over alternatives or provide exclusions. No alternative tools are mentioned, so the usage guidance is only implied.
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?
The description discloses a critical behavioral trait: the endpoint is known to fail upstream and returns an error. This adds transparency beyond the schema, but with no annotations, it does not cover other safety or permission aspects.
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 concise and front-loaded, stating the tool's purpose in one sentence and adding a valuable known-issue warning. No unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description should explain what the detail list contains or how to interpret the result, but it does not. The known-issue warning is helpful, but overall the description is incomplete for a tool with no annotations.
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 all parameters are already documented. The description adds no additional parameter meaning beyond the schema, hence the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that this tool gets the monthly labor insurance detail list for an insurance unit, using a specific verb and resource. It does not explicitly distinguish from sibling tools like get_monthly_labor_pension, but the resource is well-defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. The known issue warning provides context about expected failure but does not help with tool selection or when to use.
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 provided, the description carries the full burden of behavioral disclosure. It only states the action (export) and does not clarify what 'effective' means, whether it is read-only, what output format to expect, or any side effects. This is a minimal disclosure for an export 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?
The description is a single, focused sentence that immediately states the purpose. It is appropriately sized, front-loads the core action, and contains no superfluous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (one optional parameter) and absence of an output schema, the description is minimally adequate but lacks detail on the meaning of 'effective' and the expected return payload. It is sufficient for a simple export tool, but would benefit from a brief note on what data is included or excluded.
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 already documents the single 'language' parameter with a description ('Language code') and a default value, achieving 100% coverage. The tool description adds no additional parameter semantics, so the baseline of 3 applies.
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 exports effective employee expatriation assignments, using a specific verb and resource. It distinguishes from sibling tools by focusing on expatriation specifically, which is a unique domain among the listed export tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as export_employees or list_active_employees. It does not mention exclusions, prerequisites, or typical use cases, leaving the agent to infer usage.
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 carries the full burden. It only states a read operation with a date filter, but does not disclose whether it returns all employees, what response format to expect, any authorization requirements, or potential pagination/limits. This is minimal behavioral disclosure for a tool with no annotation support.
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 a single, front-loaded sentence that avoids unnecessary words. It conveys the essential action and scope without verbosity, earning full marks for conciseness and structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description should provide more context about the tool's results (e.g., employee scope, aggregation level, or any caveats). It currently only mentions date range and approval status, leaving significant gaps for an agent to know what to expect.
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% and both parameters (start_date, end_date) have clear format descriptions. The description adds the 'approved' filter but does not provide additional meaning beyond the schema, so the baseline score 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 ('Get') and resource ('approved overtime records') and specifies the date range scope. This clearly distinguishes it from sibling tools like get_leave_history and get_attendance_history, 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 Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for retrieving approved overtime within a date range, but it does not explicitly state when to use it over alternatives or mention any exclusions (e.g., pending overtime). The absence of explicit 'use this when' guidance leaves usage context implied rather than clearly defined.
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?
With no annotations, the description carries the transparency burden. It discloses a valuable behavioral trait: the tool wraps an endpoint that returns 404 on the PRE Foundation backend, and it explains the default behavior for missing as_of_date. However, it does not mention return format, pagination, permissions, or error behavior beyond the known issue.
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 highly concise: two sentences, front-loaded with the core purpose and followed by a critical known-issue note. Every sentence earns its place without redundancy or 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?
For a simple tool with no required parameters and no output schema, the description covers purpose, default behavior, and a known issue. It does not explain the return structure (e.g., fields in each employee object), which could be important for an agent, but given the tool's simplicity, the description is reasonably 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 covers 100% of parameters, so the baseline is 3. The description adds no meaning beyond the schema; it repeats the default for as_of_date but does not enrich dept_code semantics or offer format constraints. It provides minimal added value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'List active employees as of a date', which identifies the verb (list), resource (active employees), and temporal scope. It does not, however, differentiate from the sibling tool 'list_active_employees' which likely has a similar purpose, so it loses a point for lacking explicit distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It does not mention that 'list_active_employees' might be preferred, or any exclusions or prerequisites. The known issue note hints at unreliability but does not offer alternatives or usage conditions.
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 alone must disclose behavioral traits. It mentions pagination but not the output format, what constitutes a 'change order', authentication requirements, or behavior for empty results. This is minimal and leaves significant ambiguity for a tool with no other context.
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 a single sentence that leads with the verb and resource, includes the key scope (date range) and a behavioral note (paginated), with no wasted words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a moderately simple export tool with well-described parameters, the one-sentence description is adequate but lacks details on output format, pagination mechanics, or a definition of 'change orders'. Given the absence of an output schema and annotations, more context would improve completeness.
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 input schema provides 100% descriptive coverage for all five parameters, so the baseline is 3. The description adds a little context by linking the date range and pagination to the parameters, but it does not offer additional per-parameter meaning beyond what the schema already states.
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 the specific verb 'Export' and identifies the resource as 'employee change orders', further constrained by 'within a date range' and 'paginated'. This clearly differentiates it from sibling tools like export_employees or export_organization_changes, which target different data.
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 a use case (exporting employee changes within a date range) but does not explicitly state when to use this tool over alternatives, nor does it mention exclusions. A clear context is present, but no alternatives are noted, so it falls short of a 4.
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 are provided, so the description carries the full transparency burden. It does disclose the upstream failure issue ('Expect a server-side error response until MAYO fixes it'), which is a significant behavioral trait. However, it omits other useful context such as whether authentication is required, what the response format looks like on success, or any rate limits, leaving the transparency incomplete.
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 core action and followed by a critical warning. Every word earns its place, with no redundancy or excessive detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-oriented tool with three parameters and no output schema, the description covers the primary purpose and a critical known issue. However, it lacks guidance on when to prefer this tool over related monthly-benefit siblings and does not describe what a successful response contains (beyond 'detail list'), leaving some context missing.
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 input schema already includes descriptions for all three parameters (search_date, insurance_no, language) with 100% coverage. The description adds no additional parameter semantics beyond what the schema provides, so the baseline score 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 'Get' and identifies the resource as 'monthly labor pension (勞退) detail list for a unit, which clearly establishes what the tool does. This distinguishes it from sibling tools like get_monthly_labor_insurance and get_monthly_nhi, which target different benefit types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives such as get_monthly_labor_insurance or get_monthly_nhi. The known issue warning is operationally relevant but does not help with tool selection or context of use.
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 are provided, so the description carries the transparency burden. It correctly implies a read-only export operation and adds the 'core fields only' constraint. However, it does not explain what 'effective' means, nor does it describe the output format or any pagination/limitation behavior.
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 a single sentence of nine words. It is front-loaded with the verb and resource, contains no filler, and every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and only a brief description, the tool lacks essential context. The agent is left unsure what 'core fields' includes, what 'effective' means (e.g., current snapshot vs. historical), and how the language parameter might alter output. This ambiguity could lead to incorrect tool selection among similar export options.
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 fully describes the only parameter 'language' with a clear description, so the baseline is 3. The description does not add any extra meaning to the parameter, such as how language affects the exported data or whether it is a display language or data language.
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 action ('Export'), the resource ('effective department master data'), and the scope ('core fields only'). This distinguishes it from broader organization export tools like export_organization_full and export_organization_with_employees.
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 usage context is implied through the resource name ('department master data'), but there is no explicit guidance on when to use this tool versus alternatives. No comparisons or exclusions are provided to help the agent choose among the many export tools.
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?
With no annotations provided, the description carries the full burden. It clearly indicates this is a read operation and lists example return types, but it does not mention response format, pagination, data limits, or any edge cases like multiple exceptions per employee. The use of 'etc.' leaves the exact scope ambiguous.
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 a single, front-loaded sentence that delivers the purpose and scope without unnecessary words. Every word earns its place, making it highly concise.
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 simple tool with two well-documented parameters and no output schema, the description covers the essential elements: what it gets (exceptions) and the date range constraint. It could be more complete by specifying the return structure, but it is adequate for this low-complexity 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?
The input schema already provides 100% coverage for both parameters, including format and purpose. The description simply restates that the tool works within a date range, adding no extra semantic value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Get' with the resource 'attendance exceptions' and provides examples like late, early leave, and missed punch. It clearly distinguishes itself from sibling tools such as get_attendance_history or get_attendance_summary by focusing on exceptions, though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'within a date range' implies when to use this tool, but it does not explicitly state when not to use it or provide alternatives. The intended scenario is clear for retrieving exception records, but there is no comparison with related attendance tools.
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 provided, the description carries the full burden of behavioral disclosure. It only says 'Export all employees' education records,' which implies a read-only retrieval but does not explain output format, pagination, authentication requirements, or lack of side effects. The minimal wording leaves the agent to infer expected behavior.
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 a single, concise sentence with no redundant words. Every word contributes to understanding the tool's purpose. It is appropriately front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter export tool, the description covers the core function but leaves out context about the output format (e.g., file type), whether it modifies anything, and any caveats. With no annotations or output schema, the description is the sole source; while it's enough for a simple export, it could detail the return style.
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?
The tool has zero parameters, so the schema is trivially complete. The description adds semantic meaning by clarifying the scope ('all employees'), which acts as a built-in filter. Even though no parameters need explanation, the description successfully conveys what data will be returned.
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 action (export), the resource (education records), and the scope (all employees). This distinguishes it from sibling export tools like export_working_history or export_employees, which target different data categories.
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 the tool is used when you need education records for all employees, but it does not explicitly state when to use it versus alternative tools or provide exclusions. Since there are many export siblings, a brief note on when this is preferred would improve guidance.
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 openly reveals a critical known issue: the route returns 404 on the PRE Foundation backend and is kept only for future deployment. This is valuable honesty that saves the agent from pursuing a broken call. However, it does not disclose return structure, pagination, or other behavioral quirks, leaving some gaps.
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: the first states the purpose clearly, the second documents a known issue. Every sentence earns its place, information is front-loaded, and there is no verbose padding. This is a model of concise, useful description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description is reasonably complete for a simple read operation. It explains the purpose and warns about the current 404 issue. However, it does not describe the return format or expected hierarchy structure, and it fails to mention that get_organization_snapshot_as_of may be a working alternative. This leaves the agent with an incomplete picture of the tool's behavior and alternatives.
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 input schema provides 100% coverage with descriptions for all three parameters (dept_code, query_date, include_ineffective). The description adds no additional parameter meaning, meriting the baseline score of 3. The date-focused purpose aligns with the required query_date but does not explain syntax or interactions beyond the 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 a specific action: 'Get the organization hierarchy as of a specific date.' This is a distinct verb and resource, and the 'as of' phrasing indicates a temporal snapshot. It separates this tool from siblings like list_active_employees or export_departments, though it does not explicitly contrast with get_organization_snapshot_as_of, which may be a near duplicate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives. The known issue explains the tool is currently non-functional in the PRE Foundation environment, but it does not direct the agent to an alternative or state the intended use case beyond the purpose. The absence of when-to-use or when-not-to-use guidance other than the 404 warning leaves the agent without decision support among sibling tools.
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 are present, so the description carries the full burden. It discloses a critical behavioral trait: the 404 known issue on the PRE backend. However, it does not describe other behavioral aspects like response format, pagination, or read-only nature (though 'List' implies read-only). The known issue adds real value beyond the schema but the description could be more forthcoming.
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, with a clear first sentence and a relevant known-issue second sentence. Every sentence earns its place, though the known-issue block adds length. It is efficient and well-structured for its complexity.
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 (2 params, no output schema, no nested objects), the description sufficiently covers purpose and a critical environmental limitation. It does not explain return values, but that is arguably inferred from the tool name. The known issue is a key piece of context that makes this adequately 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?
Schema description coverage is 100%, so the schema fully documents both parameters. The description merely restates their purpose ('as of a given date', 'scoped to a department') without adding new syntax or format details, thus providing no added value over the schema. Baseline 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 ('List'), a clear resource ('active employees'), and qualifiers ('as of a given date', 'optionally scoped to a department'). This distinguishes it from siblings like list_resigned_employees and search_active_employees by specifying 'active' and 'list' rather than 'search'.
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 known issue provides implied usage guidance: the tool is only usable when the Report Center endpoints are deployed (MAYO), not on PRE where it returns 404. However, there is no explicit mention of alternatives or when to prefer other sibling tools, leaving the guidance somewhat implicit.
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 carries the full burden of behavioral disclosure. It only states the action 'Export' without specifying whether it is read-only, what the output format is, whether it includes resigned employees (given sibling tools distinguish active vs resigned), or if there are any side effects. This is a significant transparency gap.
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 a single, front-loaded sentence with no redundant words. It efficiently conveys the essential action and scope, earning a top score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no parameters and no output schema, the description should explain what the tool returns or how the export is delivered (e.g., file, JSON, CSV). It does not, leaving the agent uncertain about the result structure. The description is adequate for basic selection but incomplete for confident invocation.
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?
The tool has zero parameters, and the description adds the key semantic that the export covers 'all employees,' which matches the empty schema. Since the schema provides no constraints, the description adds value by clarifying the scope, justifying the baseline of 4.
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 a specific verb ('Export') and a specific resource ('all employees' work-experience records'), making it distinct from sibling tools like export_education_history or export_employees. The scope ('all employees') is explicit and matches the absence of parameters.
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 this tool: when there is a need to export work-experience records for all employees. It does not explicitly list exclusions or alternatives, but the noun phrase 'work-experience records' distinguishes it from other export tools, giving the agent sufficient guidance.
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?
With no annotations, the description carries the full burden of disclosure. It explains the output conceptually (shift calendar categories) but does not mention permissions, pagination, or response structure. The read-only behavior is implied but not explicitly stated.
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 a single, front-loaded sentence with no redundant words. Every word contributes meaning, making it highly concise and well-structured.
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 simple tool with two well-defined parameters and no output schema, the description sufficiently conveys the tool's purpose and result. It could benefit from noting that it returns per-employee data, but the core context is present.
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 input schema already fully describes both parameters with format details, so the description adds minimal semantic value beyond the phrase 'for a range.' Baseline of 3 is appropriate given 100% schema coverage.
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 ('Get') and resource ('each employee's assigned shift calendar') and clearly defines the scope ('for a range'). It also distinguishes itself from sibling tools like get_attendance_history by specifying shift calendar content (work day/rest day/holiday).
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 by stating what the tool returns, but it does not explicitly state when to use this tool over alternatives or provide any exclusionary guidance. Sibling tools like get_attendance_history could overlap, yet no distinction is made.
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 are present, so the description must carry the transparency burden. It does disclose that only approved and pending applications are returned, which is useful behavioral context. However, it does not mention aspects like read-only safety, output format, or pagination, which are relevant for a 'get' tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tightly worded sentence that gets straight to the point. There is no fluff, and the key information (statuses and date range) is presented clearly.
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 simple two-parameter read tool, the description sufficiently conveys purpose and scope. It does not explain return values, but the absence of an output schema and the simplicity of the request reduce the need for further detail. The only minor gap is the unexplained 'leave_historyV2' marker.
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 input schema already provides full descriptions for both parameters (start_date and end_date, including format), so the coverage is 100%. The description's phrase 'within a date range' adds no additional semantic detail beyond what the schema states.
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: 'Get approved and pending leave applications within a date range'. The verb 'get' and the resource 'leave applications' are specific, and the mention of 'approved and pending' and 'date range' distinguishes it from sibling tools like get_attendance_history or get_trip_history.
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 provides no explicit guidance on when to use this tool versus alternatives. The usage context is implied by the name and the description, but there is no mention of exclusions or preferred situations compared to sibling tools.
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?
With no annotations provided, the description carries the full burden. It discloses a read-only operation ('get') and key scope, but lacks details on response structure, pagination, or data coverage. The term 'salary insurance' is ambiguous as it does not specify whether it includes labor, health, or pension contributions.
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 a single efficient sentence with no filler. It front-loads the verb 'get' and the target object, making it easy to parse and understand at a glance.
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?
The tool is a straightforward read with one required and four optional parameters, and the description clearly conveys the core scope (per-employee, date-based). However, the absence of an output schema and annotations means the agent may not know what fields the 'detail' includes, which slightly affects completeness.
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 input schema provides 100% coverage with descriptive titles and comments (e.g., search_date format, dept_code filter). The description adds no additional parameter semantics, so the baseline of 3 applies since the schema already documents the parameters.
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 'get' with a precise resource 'per-employee salary insurance contribution detail' and a temporal scope 'for a given date'. This clearly differentiates it from sibling tools like get_monthly_labor_insurance which focus on monthly aggregates.
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 when to use the tool: when needing per-employee insurance contribution details for a specific date. However, it does not explicitly state when not to use it or point to alternatives, leaving the guidance implied rather than explicit.
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 are provided, so the description carries the full burden. It discloses that only core fields are exported, which is a behavioral trait, but it does not clarify what 'effective' means (e.g., active vs. all employees) or mention output format, pagination, or data limits. The statement 'effective employee master data' is somewhat ambiguous but still adds partial transparency beyond the tool name.
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 a single concise sentence that conveys the action, resource, and scope. Every word contributes meaning with no redundancy or filler. It is ideally sized for an API tool description.
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 one optional parameter and no output schema, the description is fairly complete. It explains what the tool exports and the field scope. The main gap is the ambiguity around 'effective' and the lack of explicit mention of return structure, but given the simplicity and sibling context, the description is largely sufficient.
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 single parameter 'language' is fully described in the input schema with a default and description. The tool description does not add any additional meaning beyond the schema, but since schema coverage is 100%, the baseline of 3 is appropriate. No further clarification is needed.
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 specifies a clear action ('Export'), a resource ('effective employee master data'), and a scope ('core fields only'). This distinguishes it from siblings like export_employees_full, which presumably exports all fields, and list_active_employees, which might return a different format or filtered subset. The qualifier 'effective' and 'core fields only' add semantic precision.
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 phrase 'core fields only' implicitly communicates when to use this tool versus alternatives: if only essential fields are needed, select this export; if full data is required, use the full export sibling. However, there is no explicit directive like 'use this when' or 'for complete data, use export_employees_full,' leaving some room for inference.
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 are provided, so the description carries the burden. It discloses that only manually-added or corrected clock-in records are returned, which adds behavioral context beyond the tool name. However, it does not mention permissions, return format, or filtering behavior beyond the date range, which limits transparency for a read operation.
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 a single concise sentence that is front-loaded with the verb and resource, then scoped by the date range. Every word contributes to the meaning with no redundancy.
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?
The tool is low-complexity with two well-documented parameters and no nested objects. The description clarifies the record type (manual/corrected) but does not explain return values or inclusivity of dates. Overall, it is adequate for a simple list tool, though slightly more detail would be ideal.
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 provides full descriptions for both parameters (start_date and end_date) with 100% coverage. The description adds 'within a date range' but does not provide new meaning beyond what the schema already specifies, 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 'Get' and clearly identifies the resource as 'manually-added or corrected clock-in records' within a date range. This distinguishes it from broader siblings like 'get_attendance_history' by specifying the exact subset of records targeted.
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 indicates the scope (date range, manual/corrected records) but does not explicitly mention when to use this tool over alternatives. The niche is evident from the name and description relative to sibling tools, but there are no explicit exclusions or alternative references.
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 are provided, so the description carries the full burden. It explicitly discloses a critical behavioral trait: the endpoint is known to fail upstream and will likely return a server-side error. This goes beyond a typical read-only description and alerts the agent to an important operational caveat.
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: the first states the purpose clearly, the second adds a critical warning. Every word is functional, with no redundancy or unnecessary detail.
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 simple read tool with 3 parameters and no output schema, the description provides the essential purpose and a critical failure warning. It does not describe return values, but the known issue is the most important contextual detail an agent needs for this 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?
The input schema already provides descriptions for all three parameters (search_date, insurance_no, language), achieving 100% coverage. The description adds no additional parameter semantics, so the baseline of 3 applies.
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 ('Get') and identifies the resource ('monthly National Health Insurance (健保) detail list for a unit'), clearly distinguishing it from sibling tools like get_monthly_labor_insurance and get_monthly_labor_pension. The scope ('for a unit') adds clarity.
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 monthly NHI detail is needed, but provides no explicit guidance on when to use this tool versus alternatives. No exclusions or alternative tool mentions are present, so usage context is only implicit.
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?
With no annotations, the description carries the burden of transparency. It discloses input normalization (truncation of full dates to months, internal YYYY/MM format), which is useful. However, it does not state whether the operation is read-only, mention any permissions, output format, or potential side effects, so transparency is partial.
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 tool's purpose and then succinctly explaining input formatting. No wasted words; every sentence earns its place.
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?
The tool is straightforward (two-parameter range query) and the description gives enough context for basic use. However, with no output schema, the description does not specify the return structure or any limits (e.g., pagination), leaving some minor gaps for a complete understanding.
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?
While the schema provides 100% coverage of both parameters with format descriptions, the description adds extra semantics: explaining that YYYY-MM-DD inputs are truncated to months and that the tool converts to YYYY/MM internally. This goes beyond the schema's literal format notes, enriching parameter understanding.
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 'Get the salary and bonus distribution roster across a month range,' using a specific verb and resource. It distinguishes itself from sibling tools like get_monthly_payroll_report and list_active_employees by focusing on the salary/bonus distribution roster over a range.
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 gives context on input format but no explicit guidance on when to use this tool vs alternatives. It implies the use case (salary/bonus roster for a month range) but does not mention exclusions or alternative tools, leaving usage decisions to the agent.
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?
With no annotations, the description carries the full burden. It discloses a significant behavioral trait: the API route returns 404 on PRE Foundation backend and is kept for future MAYO deployment. However, it does not describe return format, pagination, authentication, or other operational behavior.
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 concise, front-loaded with the main purpose, and followed by a critical known-issue note. Three sentences total, with no wasted words.
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 simple list tool with 3 parameters and no output schema, the description is mostly complete. The known issue provides essential deployment context, but it lacks explicit return type information or a clear directive like 'do not use in PRE Foundation'—the caveat is implied rather than stated.
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% and all parameters (start_date, end_date, dept_code) have clear descriptions. The description adds no additional parameter semantics beyond what the schema already provides, so baseline 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 'List employees who resigned or took leave within a date range,' using a specific verb and resource. This distinguishes it from siblings like list_active_employees and search_active_employees by focusing on resigned/leave status.
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 environment-specific guidance via the known issue (404 on PRE Foundation backend), implying the tool should not be used there. It does not explicitly name alternatives or state when-not-to-use beyond the environment caveat, but the context is clear.
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 that the tool 'fans out' to multiple data sources and returns sections under dedicated keys, which is valuable behavioral context. It does not mention permissions, performance, or exact output structure, but for a read-only aggregation tool, the disclosed fan-out behavior and output organization are sufficient transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main purpose, and every sentence earns its place. It introduces the tool's function, explains the fan-out, and describes the return format, with no fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so the description should compensate by explaining return values. It states sections are returned under dedicated keys, but does not specify the exact key names or the structure/content of each section. Given the complexity of a consolidated summary, this leaves some ambiguity about the response format, though it is sufficient for basic invocation.
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 baseline is 3. The description adds minor value by linking the optional boolean parameters to the underlying data sections (attendance_abnormal, overtime_records, leave_history), but this largely overlaps with the existing parameter descriptions that already specify what each include flag does. No additional parameter semantics are provided.
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 'Build a consolidated attendance picture for a date range', which is a specific verb+resource purpose. It also distinguishes from sibling tools by explicitly mentioning fan-out to `attendance_history`, `attendance_abnormal`, `overtime_records`, and `leave_history`, which directly differentiates it from the individual list tools.
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 implies usage for a consolidated attendance view across multiple data sources, which is clear context. It mentions the optional inclusion of abnormal, overtime, and leave sections, but does not explicitly state when to use this instead of calling the individual `get_attendance_*` tools. No exclusion or alternative guidance is given, but the aggregation purpose is evident.
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 this is a read operation (implied by 'Get'), returns raw records, and spans all employees without filtering. This goes beyond the schema and gives the agent a clear behavioral picture, though it omits details like pagination or output shape.
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 a single, front-loaded sentence with no filler. It effectively communicates the core action and scope, earning every word.
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?
This is a simple tool with two well-documented parameters and no output schema. The description sufficiently indicates that the return will be raw clock-in/out records for all employees, which is enough for an agent to select and invoke the tool. It lacks details on pagination or response format, but these are not critical for initial tool selection given the tool's simplicity.
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% with clear format descriptions for both parameters. The tool description adds only the phrase 'within a date range,' which adds little beyond the schema's date range descriptions. Baseline 3 is appropriate since the schema already documents the parameters well.
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 uses the verb 'Get' with a specific resource ('raw clock-in / clock-out records') and scope ('across all employees within a date range'). It distinguishes itself from sibling tools like get_attendance_summary and get_attendance_abnormal by emphasizing 'raw' records.
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 states the tool returns raw records for all employees, which gives clear context for when to use it (e.g., needing actual punches rather than summaries). However, it does not explicitly mention when not to use it or name alternatives such as get_attendance_summary for aggregated data.
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, the description carries the full burden and does well: it explains the composite nature, what data is combined, and proactively warns about the current 404 failure. It does not disclose permissions or pagination, but for a read-only snapshot tool this is sufficient and adds real value.
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 front-loaded with the purpose, then explains the combination, and finally the known issue. No word is wasted; each sentence provides distinct, necessary information. This is exemplary conciseness.
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 composite tool with no output schema and no annotations, the description adequately explains what is returned and the current limitation. It could specify the behavior when dept_code is null (which the schema leaves ambiguous), but overall it is complete enough for an agent to decide when and how to invoke it.
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 provides 100% coverage for both parameters (dept_code, as_of_date) with types and descriptions. The tool description does not add meaning beyond what the schema already states; it only restates the date concept. Baseline 3 is appropriate as 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.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Capture') and resource ('the organization tree and active headcount as of a specific date'), and distinguishes itself from siblings by explaining it combines `organization_tree` and `active_employees`. This is unambiguous and action-oriented.
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 implies usage context: use this composite when you need both the org hierarchy and active headcount in one call. It also provides a critical 'when-not' by disclosing the KNOWN ISSUE (404s) and that it will work after redeployment. However, it does not explicitly name alternatives ('use get_organization_tree if you only need the tree'), so it falls short of a 5.
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 are provided, so the description carries the full burden. It discloses a known upstream failure and warns about server-side errors, which is valuable behavioral transparency. However, it doesn't explicitly state read-only status or any additional behavioral details like authentication or pagination.
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: one for purpose and one for the known issue. It is front-loaded and every sentence earns its place, with no filler or repetition.
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 2-param tool with no output schema, the description gives the essential purpose and the critical known-issue warning. It lacks detailed return structure, but the failure warning is arguably more important for an agent deciding whether to call it.
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 already covers both parameters with descriptions (100% coverage), so the baseline is 3. The description's mention of 'date range' adds no new meaning beyond the schema's start_date/end_date fields.
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 'Get business trip / field-work records within a date range,' specifying the verb (Get), resource (business trip / field-work records), and scope (date range). This distinguishes it from sibling tools like attendance or leave history.
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: it retrieves trip records for a date range, implying when to use it. It doesn't explicitly name alternatives or exclusion criteria, but the resource type is distinct from the sibling tools, so ambiguity is low.
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 are provided, so the description carries the full burden. It discloses that the tool returns data for every active employee and lists the specific information returned (shift, rest-day rule, attendance-group). This gives the agent a good sense of the operation's behavior, though it does not mention authorization, pagination, or error handling.
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 long, front-loaded with the action and resource, and includes no filler or redundant information. Every sentence contributes meaningful guidance.
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?
The tool is simple (one parameter, no output schema) and the description adequately explains what is returned and for whom. It could be slightly more complete by describing the output structure (e.g., one entry per employee), but this is a minor gap for a straightforward read-only configuration query.
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% for the single parameter search_date, and its format is already documented. The description adds value by explaining that this date determines which configuration applies and that it applies to active employees on that date, which clarifies the parameter's semantic role beyond the 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?
Description starts with a specific verb 'Get' and names the exact resource: 'each employee's base attendance configuration'. It also clarifies the temporal applicability ('as of a given date') and the returned fields (shift, rest-day rule, attendance-group information), which distinguishes it from sibling attendance tools that handle history, abnormal records, or summaries.
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 context is clear: use this tool to retrieve base attendance configuration for all active employees on a specific date. However, it does not explicitly state when to prefer this over siblings like get_attendance_history or get_attendance_abnormal, nor does it name any alternatives. This is a clear 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 behavioral burden. It discloses that it calls PA and company-wide Working and Education exports, then filters down to the target employee—revealing multi-call behavior and internal data scope. It could mention potential performance implications or authentication needs, but the core mechanics are 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 three sentences, each with a distinct role: first defines the purpose, second explains the internal mechanics, third gives a concrete when-to-use scenario. No filler or redundancy.
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 moderately complex aggregation tool with no output schema, the description covers purpose, implementation, and usage context. It does not specify the exact return structure of the profile, but given the clarity of the aggregation source fields, this is a minor gap rather than a blocking issue.
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 reinforces that employee_number is the key filter and the whole point of the aggregation, but it doesn't add new semantics beyond the schema. The language parameter is not discussed further in the description, so no extra value is provided there.
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 aggregates one employee's master, work history, and education into a single profile. The verb 'aggregate' plus the specific resource scope distinguishes it from sibling raw exports and listing tools.
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?
It explicitly provides a usage scenario: 'Useful when an assistant needs a complete picture without having to call three raw tools and join the results.' This tells the agent when to use this tool over alternatives such as export_working_history or export_education_history.
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?
Without annotations, the description carries the burden of behavioral disclosure. It adds valuable context by specifying accepted input formats (ISO YYYY-MM or YYYY-MM-DD truncated) and the conversion to MAYO's YYYY/MM format. It does not explicitly state read-only nature or return details, but the conversion behavior is a useful addition beyond what the name implies.
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 extremely concise, with the first sentence clearly stating the purpose and the second sentence providing essential usage and format details. No filler or redundant information.
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 signature (1 parameter, no output schema), the description is nearly complete. It covers purpose, input format, conversion behavior, and alternative tool for multi-month needs. It could mention the output shape, but for a report-type tool the name and 'roster' hint suffice, and the lack of output schema means the description need not over-explain.
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%, so baseline is 3. The description adds meaningful semantics by noting that the parameter accepts both YYYY-MM and YYYY-MM-DD (truncated) and is converted to YYYY/MM, which is not mentioned in the schema description. This helps the agent understand the flexibility and internal transformation of the input.
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 retrieves a salary and bonus distribution roster for a single month, using a specific verb and resource. It distinguishes itself from the sibling get_salary_bonus_list by explicitly mentioning the single-month scope and pointing to the alternative for multi-month ranges.
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?
The description explicitly directs users to use the raw get_salary_bonus_list tool for multi-month ranges, providing a clear alternative. The context that this tool is for a single month is stated up front, making it easy to choose between tools.
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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