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 declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds meaningful behavioral detail beyond annotations by disclosing delimiter auto-detection and quoted-field handling, which are important parsing behaviors an agent would otherwise not know.
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 a single concise sentence that front-loads the core action ('Parse CSV data into JSON') and appends the two distinguishing details. No filler or redundant information is present.
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?
With an output schema present and annotations covering readOnly/idempotent behavior, the description plus structured data provides a complete picture for most usage. Minor missing context includes how malformed CSV is handled or behavior when firstRowHeaders is false, but these are not critical 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?
Schema description coverage is 100%: both the 'csv' and 'firstRowHeaders' parameters are documented in the schema. The description adds only the 'quoted-field handling' detail, which is helpful but not essential to parameter understanding, so the baseline score of 3 is appropriate.
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 ('Parse') and resource ('CSV data into JSON'), and adds distinctive details ('delimiter auto-detection and quoted-field handling') that clearly differentiate it from sibling conversion tools. An agent immediately understands what this tool does.
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 implies the tool should be used when CSV data needs to be converted to JSON, especially when delimiters are unknown or quoted fields are involved. However, it provides no explicit guidance about when to prefer alternatives or 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 target a clearly distinct resource and action, so an agent can usually tell them apart. A few close pairs exist (generate_test_bsn vs generate_brp_test_data, html_to_pdf vs url_to_pdf, read_page vs url_screenshot), but the descriptions draw clear boundaries.
Tool names consistently use snake_case and mostly follow a verb_noun pattern like generate_slug, validate_email, or pdf_merge. There are a few noun-style exceptions such as base64, csv_to_json, and password_strength, but no mixed casing or chaotic naming.
40 tools is heavy and exceeds the comfortable selection range for most agents. The server presents itself as a general-purpose utility toolbox, so the breadth is defensible, but the large flat tool list creates real navigation burden.
The toolkit covers common encoding, conversion, image/PDF, validation, Dutch-specific test data, memory, and workflow needs quite well. Minor gaps like PDF text extraction or JSON-to-CSV conversion exist, but agents can typically work around them.