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?
Annotations already indicate readOnlyHint=false (write operation), openWorldHint=true (accesses external URLs), and destructiveHint=false. The description adds valuable context: the cost (~$0.10) and the output property (transparent result). It doesn't disclose potential failure modes, but with annotation coverage, this is sufficient.
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 a single, front-loaded sentence that conveys the function, output, and cost without any filler. Every word contributes value, exemplifying ideal conciseness.
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 (one parameter, output schema available), the description is mostly complete. It could mention that the image must be publicly accessible, but that is already covered in the schema. The cost and transparent result are included, and the output schema handles return value details.
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?
Schema description coverage is 100%: the 'image' parameter is described as 'Public URL of the image'. The description does not add additional meaning beyond that, so it relies on the schema. This meets the baseline for high coverage.
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 ('Remove') and resource ('image'), clearly stating the output is a transparent result. It distinguishes itself from sibling tools like ai_image or ai_vision by focusing on background removal, a unique function.
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 provides clear context: use this tool when you need to remove an image background and get a transparent result. It doesn't explicitly name alternatives or exclusions, but none of the sibling tools offer this specific capability, so the usage intent is unambiguous.
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.
Four image generation tools, three video tools, and five 'ask' tools create significant overlap. Although descriptions specify the model, an agent must carefully compare prices and capabilities to choose correctly, making misselection likely.
All tool names use snake_case, but patterns are mixed: some start with verbs (remove_bg, scrape_page), some with nouns (crypto_prices, market_snapshot), and many use ai_/ask_ prefixes. Model suffixes like flux, gpt, pro, kling are descriptive but not systematically applied.
24 tools is heavy, inflated by near-duplicate variants for image, video, and LLM queries. While the broad scope justifies a large count, the redundant tools could have been consolidated.
The toolset covers a wide range of media and data tasks: image, video, music, voice, vision, LLM, web, crypto, domain, and endpoint discovery. Notable gaps like speech-to-text or image editing exist, but the surface is fairly complete for a general-purpose media toolkit.