recruitment-fee-calcStaffing agency fee, refund terms, and KPI back-calculation.
| Name | Required | Description | Default |
|---|---|---|---|
| feeRate | No | Fee rate (%) | |
| feeType | No | Fee type | percentage |
| fixedFee | No | Fixed fee in JPY (when feeType=fixed) | |
| monthlyTarget | No | Monthly target placements | |
| averagePlacementFee | No | Average placement fee in JPY | |
| theoreticalAnnualSalary | Yes | Theoretical annual salary in JPY |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / $schemaAdded value: +"http://json-schema.org/draft-07/schema#"Input schema / properties / averagePlacementFeeAdded value: +{
+ "default": 0,
+ "description": "Average placement fee in JPY",
+ "minimum": 0,
+ "type": "number"
+}Input schema / properties / feeRateAdded value: +{
+ "default": 35,
+ "description": "Fee rate (%)",
+ "maximum": 100,
+ "minimum": 0,
+ "type": "number"
+}Input schema / properties / feeTypeAdded value: +{
+ "default": "percentage",
+ "description": "Fee type",
+ "enum": [
+ "percentage",
+ "fixed"
+ ],
+ "type": "string"
+}Input schema / properties / fixedFeeAdded value: +{
+ "default": 0,
+ "description": "Fixed fee in JPY (when feeType=fixed)",
+ "minimum": 0,
+ "type": "number"
+}Input schema / properties / monthlyTargetAdded value: +{
+ "default": 0,
+ "description": "Monthly target placements",
+ "minimum": 0,
+ "type": "number"
+}Input schema / properties / theoreticalAnnualSalaryAdded value: +{
+ "description": "Theoretical annual salary in JPY",
+ "minimum": 0,
+ "type": "number"
+}Input schema / requiredAdded value: +[
+ "theoreticalAnnualSalary"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It mentions the subject areas (fee, refund terms, KPI back-calculation) but does not state that it calculates results, what transformations occur, or any side effects. This is minimally informative but not a complete tautology.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short and free of redundancy, but it is under-specified for a tool with six parameters. It reads more like a tagline than a functional specification, so while concise, it sacrifices necessary substance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity and lack of an output schema, the description does not explain what the tool returns or how the calculation works. It provides only a vague list of topics, leaving significant gaps for an agent attempting to use or invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does 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 adds no parameter-specific meaning beyond the schema's own descriptions, which are already present and adequate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the domain (staffing agency fees, refund terms, and KPI back-calculation) with specific nouns, making the tool's scope reasonably clear. However, it lacks an explicit verb and does not differentiate it from sibling fee calculators, so it falls short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does 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 alternative fee calculators or other tools. The description offers no context, prerequisites, or exclusions, leaving the agent without direction for selecting it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Multiple tools have overlapping purposes, such as bg-remover, bg-remover-pro, pro-matting, and takumi all performing background removal, and upscaler/upscaler-pro being redundant. With 202 tools, an agent may easily select the wrong one despite detailed descriptions.
Most tools use a consistent kebab-case format with descriptive names like pdf-compress, image-resizer, and tax-return-calc. Exceptions like 'takumi', 'pro-matting', and '-pro' suffixes (bg-remover-pro, upscaler-pro) are minor deviations relative to the total.
202 tools is an extreme mismatch for an MCP server, far exceeding the typical 3-15 well-scoped range. The sheer volume makes it unwieldy for an agent to efficiently navigate and select the right tool.
The tool set provides extensive coverage across many domains, including PDF operations (20+ tools), image editing, financial calculations, e-commerce fee estimation, and YouTube utilities. Minor gaps exist in cross-tool integration, but the breadth is highly comprehensive for the apparent purpose.