remove_bgRemove the background from an image, transparent result. ~$0.10.
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | Public URL of the image |
| Name | Required | Description | Default |
|---|---|---|---|
| result | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations (readOnlyHint=false, openWorldHint=true, destructiveHint=false) already outline the overall safety profile. The description adds valuable context beyond these: it reveals the output format ('transparent result') and the financial cost ('~$0.10'), which are useful behavioral details. It does not cover rate limits or error handling, but that's acceptable for such a simple tool.
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 efficient sentence that front-loads the core action ('Remove the background'), immediately states the outcome ('transparent result'), and appends the cost. Every word earns its place; there is no superfluous text.
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 tool with a single parameter and an existing output schema, the description covers the essential context: purpose, result type, and pricing. It omits edge cases like file format constraints or size limits, but those are not critical for a basic invocation and would likely be covered by error messages or additional documentation.
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 input schema fully documents the single required parameter 'image' with the description 'Public URL of the image' at 100% coverage. The tool description does not add further parameter-specific semantics, but the schema already provides sufficient meaning, so the baseline 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 clearly states the action: 'Remove the background from an image.' It specifies the outcome ('transparent result') and even notes the cost ('~$0.10'), giving a precise, self-contained purpose. No sibling tool overlaps with this functionality, so it stands alone.
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 implies the use case: when you need to remove an image background. It does not explicitly name alternatives or exclusion criteria, but none of the sibling tools offer similar functionality, making this guidance adequate.
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 have clearly distinct purposes (image, music, video, vision, voice, etc.), but some overlap exists: ask_ai vs ask_ai_pro differ only in model strength, and web_search vs research_report both involve search with AI responses. Descriptions help clarify, though an agent could misselect in edge cases.
Tool names follow a mostly consistent snake_case pattern, with many using an 'ai_' prefix for generation tasks. However, name styles vary between verb_noun (call_endpoint, remove_bg) and noun_verb (crypto_prices, domain_info), and ask_ai/ask_ai_pro break the ai_ prefix convention. Minor deviations, but the overall pattern is readable.
At 16 tools, the server is slightly above the ideal 3-15 range but remains well-scoped for a multi-purpose utility server. Each tool has a distinct function, and the count feels manageable rather than overwhelming.
The server covers a broad set of capabilities (AI generation, web search, crypto, domain info), but it lacks lifecycle management for generated assets—there are no list/get/delete operations for previously created media, and the domain appears to be a collection of paid endpoints rather than a cohesive service.