Count working days (Mon-Fri) between two dates inclusive, optionally excluding Dutch public holidays. Exact date math for SLA, payroll, and planning questions.
| Name | Required | Description | Default |
|---|---|---|---|
| end | Yes | YYYY-MM-DD | |
| start | Yes | YYYY-MM-DD | |
| excludeDutchHolidays | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of explaining behavior. It does so well by defining the weekday range (Mon-Fri), stating that dates are inclusive, and noting the optional Dutch holiday exclusion. Minor unspecified behavior like handling of start dates after end dates is not covered, but the core behavior is clear.
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 two sentences with no filler. The primary calculation is front-loaded, and the use-case sentence adds practical context without repeating schema information.
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?
For a low-complexity calculation tool with no output schema and no annotations, the description is nearly complete: it covers input semantics, inclusivity, holiday handling, and use cases. It does not explain edge cases like invalid date order, but these are minor for this tool.
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 coverage is 67%, with start and end only described as 'YYYY-MM-DD'. The description adds meaningful semantics: the two dates form an inclusive range, and the boolean controls Dutch holiday exclusion. This goes beyond the sparse schema text.
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 clearly states the action ('Count'), the resource ('working days between two dates'), and key scoping details ('Mon-Fri', 'inclusive', 'Dutch public holidays'). This makes the tool's purpose immediately distinguishable from sibling tools like dutch_public_holidays, which lists holidays rather than counting working days.
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?
The description gives concrete use contexts: 'SLA, payroll, and planning questions.' This clearly signals when the tool is appropriate, though it does not explicitly name sibling alternatives or state when not to use the tool.
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.
Most tools have clearly distinct purposes, but some pairs could be confused: html_to_pdf vs url_to_pdf both produce PDFs, read_page vs url_screenshot both fetch a URL, and generate_test_bsn vs generate_brp_test_data both generate Dutch test data. Detailed descriptions mitigate most ambiguity, so agents can usually select correctly.
All tool names follow a consistent snake_case convention with predictable verb_noun patterns (e.g., generate_*, validate_*, memory_*, url_*). The naming is uniform and makes each tool's function easily inferable from its name.
35 tools is a heavy surface for any MCP server, even a general-purpose toolbox. The large number of niche Dutch-specific tools (BRP, UPA, BSN, holidays) may overwhelm agents and increase selection complexity. The server could benefit from splitting into smaller, focused servers.
The server covers a wide range of utilities, but there are notable gaps: no hash generation, no json_to_csv counterpart, and the workflow sub-domain only offers list/run without create/delete. Core utilities are present, but some obvious counterparts and lifecycle operations are missing.