generate_test_bsnGenerate Dutch BSN test numbers that pass the elfproef (11-check) but belong to no real person. For development and test data only.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | How many test BSNs to generate, 1 to 100 (default 1) |
| Name | Required | Description | Default |
|---|---|---|---|
| bsns | No | ||
| note | No | ||
| count | No | Number of items | |
| result | No | The result, when it is not an object |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / count / descriptionAdded value: +"How many test BSNs to generate, 1 to 100 (default 1)"Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "additionalProperties": {},
+ "properties": {
+ "bsns": {
+ "items": {},
+ "type": "array"
+ },
+ "count": {
+ "description": "Number of items",
+ "type": "number"
+ },
+ "note": {
+ "type": "string"
+ },
+ "result": {
+ "description": "The result, when it is not an object"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, destructiveHint), the description discloses that the generated numbers are fake and belong to no real person, and that they satisfy the elfproef check. This adds meaningful behavioral context about the output's validity and safety.
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 concise sentences with no filler. The core action and key behavioral guarantees are front-loaded, and every sentence adds value.
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 one-parameter tool with an output schema and safety-relevant annotations. The description covers the purpose, the validation property, and the approved environment. Nothing critical is missing for correct invocation.
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 single count parameter is already fully documented in the schema with type, default, range, and description. The tool description does not add parameter-specific semantics, so the baseline 3 applies.
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 uses a specific verb ('Generate'), names a precise resource ('Dutch BSN test numbers'), and adds distinguishing detail: they pass the elfproef but belong to no real person. This clearly separates it from sibling tools like generate_brp_test_data or generate_uuids.
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 phrase 'For development and test data only' gives clear contextual constraints on when the tool should be used. It does not explicitly name alternatives or list exclusions, but the restriction is unambiguous and sufficient for a simple generation 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.
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.