convert_colorConvert a CSS color between HEX, RGB, HSL, and HSB with exact math.
| Name | Required | Description | Default |
|---|---|---|---|
| color | Yes | e.g. #4338ca or rgb(67,56,202) |
| Name | Required | Description | Default |
|---|---|---|---|
| hex | No | ||
| hsl | No | ||
| rgb | No | ||
| result | No | The result, when it is not an object |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "additionalProperties": {},
+ "properties": {
+ "hex": {
+ "type": "string"
+ },
+ "hsl": {
+ "additionalProperties": {},
+ "properties": {
+ "h": {
+ "type": "number"
+ },
+ "l": {
+ "type": "number"
+ },
+ "s": {
+ "type": "number"
+ }
+ },
+ "type": "object"
+ },
+ "result": {
+ "description": "The result, when it is not an object"
+ },
+ "rgb": {
+ "additionalProperties": {},
+ "properties": {
+ "b": {
+ "type": "number"
+ },
+ "g": {
+ "type": "number"
+ },
+ "r": {
+ "type": "number"
+ }
+ },
+ "type": "object"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior. The description adds that the conversion uses 'exact math,' which is a useful behavioral detail beyond the schema and annotations. With the output schema provided, not spelling out return values is acceptable.
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, focused sentence states the operation, the input domain, and a distinguishing precision guarantee. Every word earns its place and there is no redundant filler or repetition of schema fields.
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 one-parameter, read-only, idempotent conversion tool with an output schema, the description is complete. It states supported formats and precision, while the annotations and output schema cover safety and return structure. Nothing essential is missing 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 already documents the single parameter with an example, but the description adds value by explicitly enumerating all supported color spaces (HEX, RGB, HSL, HSB). This goes beyond the schema's example, which only mentions HEX and RGB, and clarifies acceptable input forms.
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 ('Convert') and clearly identifies the resource: CSS colors between HEX, RGB, HSL, and HSB. It also adds a meaningful qualifier ('exact math') that conveys precision expectations. This makes it easy to distinguish from image conversion tools in the sibling list.
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 clearly implicates when to use it: whenever a color needs conversion among the listed CSS color formats. While it does not explicitly name alternatives or exclusions, there is no competing color-conversion tool in the sibling set, so the context is sufficient without more guidance.
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.