csv_to_jsonParse CSV data into JSON with delimiter auto-detection and quoted-field handling.
| Name | Required | Description | Default |
|---|---|---|---|
| csv | Yes | The CSV text; the delimiter is detected automatically | |
| firstRowHeaders | No | Treat the first row as column names and return objects (default true) |
| Name | Required | Description | Default |
|---|---|---|---|
| json | No | ||
| result | No | The result, when it is not an object | |
| delimiter | No | ||
| row_count | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / csv / descriptionAdded value: +"The CSV text; the delimiter is detected automatically"Input schema / properties / firstRowHeaders / descriptionAdded value: +"Treat the first row as column names and return objects (default true)"Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "additionalProperties": {},
+ "properties": {
+ "delimiter": {
+ "type": "string"
+ },
+ "json": {
+ "items": {},
+ "type": "array"
+ },
+ "result": {
+ "description": "The result, when it is not an object"
+ },
+ "row_count": {
+ "type": "number"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish that this is read-only, non-destructive, and idempotent. The description adds value by disclosing parser behavior beyond the annotations: delimiter auto-detection and quoted-field handling, which are the key behaviors an agent needs to understand.
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?
A single, front-loaded sentence that states the core action and two meaningful behavioral details. There is no filler or redundant repetition of 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 read-only conversion tool with a simple two-parameter schema, an output schema, and safety annotations, the description is sufficiently complete. The auto-detection and quoted-field behavior are the non-obvious details that matter, and they are included.
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 input schema already documents both parameters with 100% coverage, including descriptions and defaults. The description reinforces delimiter auto-detection but does not add new parameter detail, so the schema carries the load.
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 states a specific verb and resource: 'Parse CSV data into JSON,' and adds distinguishing behaviors (delimiter auto-detection, quoted-field handling). This clearly differentiates it from sibling conversion tools like format_json or markdown_to_html.
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 intended use is implied by the description: use this tool when you need to convert CSV text into JSON. However, it does not explicitly state when not to use it or point to any alternatives, so usage guidance is adequate but not explicit.
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.