api_color_convertConvert colors between hex, rgb, hsl. ?color=#ff0000 or rgb(255,0,0) [HTTP x402 price: $0.001]
| Name | Required | Description | Default |
|---|---|---|---|
| params | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / params / additionalPropertiesRemoved value: -trueInput schema / properties / params / propertiesAdded value: +{
+ "color": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null
+ }
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool converts among three color spaces and hints at the endpoint style via the example. It does not explain what happens when an invalid color is passed or mention whether the output contains all formats or just the conversion target. The price notice adds useful context but not behavioral detail.
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 one short, front-loaded sentence followed by a concrete example and a cost note. Every piece of text earns its place, with no redundancy or filler. It is tightly packed with useful information without being long.
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?
Given the tool's simplicity, the description is mostly adequate, especially since an output schema is reported to exist. However, it is ambiguous whether the `color` param is required or optional (schema allows null). It also does not clarify exactly what the output contains (all color representations or a targeted one). Overall it is usable, not comprehensive.
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 provides zero descriptions for the single `color` parameter, so the description is the only source of meaning. The example `?color=#ff0000 or rgb(255,0,0)` directly illustrates accepted input formats. It does not explicitly show an HSL example, but the purpose statement already names hsl as a supported format.
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?
Description states a specific verb and resource: 'Convert colors between hex, rgb, hsl.' The example query `?color=#ff0000 or rgb(255,0,0)` clarifies the exact input. This clearly distinguishes the tool from the many sibling utilities, which cover other formats like JSON, YAML, CSV, or base64.
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 query-string example gives an implicit usage pattern, showing how to supply the color parameter. It does not explicitly state when to use this tool over alternatives or mention any exclusions, but the unique color-conversion purpose makes the use case evident. No prerequisites or conditions are described.
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.
Several tools have unclear boundaries: api_search and api_serp_google both return Google results, api_scrape and api_render_text both extract page text, and api_hash_multi overlaps with api_sha256 for SHA-256/SHA-512. While many tools are distinct, these overlapping pairs create real misselection risk.
Every tool follows the same api_<snake_case> pattern with no mixed conventions or casing styles. The prefix makes the server immediately recognizable and the action/resource is consistently readable across all 44 tools.
44 tools is well over the 25+ threshold for a well-scoped set, making the server feel like a grab-bag of unrelated utilities. Even though each tool is small and individually useful, the overall surface is too large and would benefit from consolidation into focused sub-servers.
The set covers many common utility categories—encodings, conversions, text analysis, web scraping, SEO, and trends—but has notable one-way gaps: CSV/YAML/TOML all convert to JSON but not back, and markdown converts to HTML but not the reverse. The broad domain makes full completeness hard to define, so only major reverse-conversion gaps stand out.