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asgard-ai-platform

mcp-mayo

get_monthly_payroll_report

Retrieve salary and bonus distribution for a single month, accepting common date formats and converting to MAYO's required format. For monthly payroll reports.

Instructions

Get the salary and bonus distribution roster for a single month.

Accepts ISO YYYY-MM (or YYYY-MM-DD, truncated) and converts to MAYO's required YYYY/MM format. Use the raw get_salary_bonus_list tool when you need a multi-month range.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
year_monthYesTarget month in `YYYY-MM` format (e.g. `2025-01`)
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.

Conciseness5/5

Is 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.

Completeness4/5

Given 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.

Parameters4/5

Does 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.

Purpose5/5

Does 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 Guidelines5/5

Does 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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