inspect_image
Inspect an image: format, dimensions, file size. Free tier: 20 calls/day per IP.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| file_base64 | Yes | The image file contents, base64-encoded. |
CompressIO — Image Compression for AI
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A4.3/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It discloses the inspection role and rate limit (20 calls/day per IP), adding useful behavioral context beyond the schema.
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, direct sentence packs purpose and rate limit with no fluff; well-structured and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter inspection tool with a clear description and complete schema, the definition is fully sufficient to guide an agent, even without an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema fully describes the single parameter (file_base64), so the description adds no additional parameter semantics, earning the baseline score for high coverage.
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?
Clearly states the tool inspects an image and lists what it examines (format, dimensions, file size), distinguishing it from sibling tools that compress, convert, or optimize.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description focuses on inspection, making the use case clear, but doesn't explicitly mention alternatives or exclusions, leaving the agent to infer from sibling names.
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
A3.9/5.0
Disambiguation4/5
The tools are mostly distinct: compress_image reduces file size, convert_image changes formats, inspect_image reads metadata, and optimize_image performs lossless optimization. Minor overlap exists because compress_image also supports format conversion, but descriptions clarify the primary intent.
Naming Consistency5/5
All four tools follow a consistent verb_noun pattern (compress_image, convert_image, inspect_image, optimize_image). The naming is uniform, predictable, and easy to understand.
Tool Count5/5
Four tools is well-scoped for an image compression server. Each tool covers a distinct core operation without unnecessary bloat, and the count feels appropriate for the domain.
Completeness4/5
The surface covers the primary operations: compress, convert, inspect, and optimize. Minor gaps exist such as missing resizing or batch processing, but the stated purpose (image compression for AI) is well covered with no obvious dead ends.