Run a free sample (free)
try_sampleFree: runs the real CLIP model on a sample image so you can judge output quality before paying.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Blixtworks
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A4/5.0
Behavior3/5
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 is free and uses the real CLIP model, but it does not describe what the sample image is, what output is returned, or any limitations. Since it is a simple no-parameter tool, the lack of detail is understandable but still leaves behavioral aspects unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that front-loads the 'Free' aspect and immediately states the function. Every word earns its place, with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 covers the essential who-what-why. It does not describe the output format, but with no output schema, adding that detail could improve completeness. Still, for a free sample tool, this is largely adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema is trivially complete. Per the rubric, a baseline of 4 applies when there are no parameters; the description need not add parameter-level semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action: runs the real CLIP model on a sample image. It clearly conveys that this is a free trial tool for evaluating output quality, distinguishing it from paid sibling tools. The 'Free' prefix highlights its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'before paying' implies a use case for evaluating the tool prior to purchase, but it does not name alternatives or specify when not to use this tool. There is no explicit guidance on how this compares to other tools like caption or embed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
A3.6/5.0
Disambiguation4/5
Most tools have clearly distinct purposes with detailed descriptions, but a few potential confusions exist: image_resize vs image_transform overlap in image manipulation, and md (HTML-to-Markdown) vs markdown_to_html are easily mistaken. Overall, however, the vast majority of tools are unambiguous.
Naming Consistency4/5
The naming convention is predominantly snake_case with a verb_noun or object_verb pattern (e.g., address_validate, json_format, wallet_balance). A few single-word names like base64, pdf, and md break the pattern, but the style remains consistent enough to be predictable.
Tool Count2/5
With 68 tools, this server is far beyond the recommended range for a coherent MCP server. Even for a general-purpose utility API, the sheer number forces agents to wade through an unwieldy list, making selection expensive and error-prone.
Completeness4/5
For a general utility toolkit, the coverage is broad: conversions, validations, image processing, web/data extraction, and blockchain functions are all present. Minor gaps exist (e.g., URL encoding/decoding, more advanced string manipulation), but agents can work around them without dead ends.