dutch_public_holidaysExact Dutch public holidays for a year, including movable feasts (Easter, Ascension, Whitsun) and King's Day rules. Use this instead of computing Dutch holidays yourself.
| Name | Required | Description | Default |
|---|---|---|---|
| year | Yes | Calendar year, 1900 to 2200 |
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| result | No | The result, when it is not an object | |
| holidays | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / year / descriptionAdded value: +"Calendar year, 1900 to 2200"Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "additionalProperties": {},
+ "properties": {
+ "holidays": {
+ "items": {},
+ "type": "array"
+ },
+ "result": {
+ "description": "The result, when it is not an object"
+ },
+ "year": {
+ "type": "number"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds behavioral context by stating the tool handles movable feasts and King's Day rules rather than returning a naive static list, which is useful beyond the structured annotations.
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 concise and front-loaded: it states the core function, the special cases it covers, and the recommended usage in two short sentences. There is no redundant filler.
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?
This is a simple single-parameter, read-only tool with a full input schema, an output schema, and annotations covering idempotency and non-destructiveness. The description is complete enough for an agent to select and invoke it 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?
The schema fully describes the 'year' parameter with a clear range and description, so the description does not need to add much. It does not go beyond the schema, but the schema already covers the parameter meaning well.
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 what the tool does: it returns the exact public holidays for a year, and it specifically calls out movable feasts and King's Day rules. This is more specific than the title alone and distinguishes the tool from generic date or holiday calculation utilities.
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 provides a clear usage signal: 'Use this instead of computing Dutch holidays yourself.' This tells the agent when the tool is appropriate, though it does not reference sibling tools such as working_days_between or list exclusions.
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.
Every tool targets a distinct resource or action, and the detailed descriptions clearly separate near neighbors like generate_test_bsn versus generate_brp_test_data, read_page versus url_screenshot versus url_to_pdf, and image_compress/convert/resize. Even with 40 tools, there is no real boundary-blurring overlap.
All names are snake_case and readable, but the set mixes conventions: verb_noun (generate_*, validate_*), noun_verb (pdf_merge, image_resize), conversion-style names (csv_to_json, html_to_pdf), and bare nouns (base64, qr_code_png). The groups are recognizable, but there is no single predictable pattern.
Forty tools is an oversized surface for an agent to consider on every call, well above the point where tool selection cost starts to hurt. The broad purpose explains the count, but many one-off utilities could be grouped or exposed selectively.
The server covers many domains—encoding, Dutch test data, image/PDF handling, memory, and workflows—but several categories are partial: there are no reverse conversions like json_to_csv or html_to_markdown, no PDF text extraction, and no workflow create/update/delete tools. Agents can work around some gaps, but notable operations are missing.