ai_image_fluxGenerate an AI image with Flux 2 (Black Forest Labs, photorealism). ~$0.08.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | What to draw | |
| aspect_ratio | No | 1:1 | 16:9 | 9:16 | 4:3 | 3:4 (default 1:1) |
| 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 non-read-only and non-destructive behavior. The description adds cost (~$0.08) and model details, but does not disclose further behavioral aspects like generation time, content policy, or rate limits. It provides some context but not comprehensive transparency.
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 sentence that efficiently conveys the core purpose, model, style, and cost. It is front-loaded with the main action and contains no filler. This is exemplary 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?
For a simple tool with two parameters and an output schema, the description covers the essential context: model, style, and cost. It could explicitly mention output format or usage constraints, but the output schema likely handles return values. The description is adequate and appropriately scoped.
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 describes both parameters with allowed values and defaults, and the description contributes no additional parameter information. Since schema coverage is 100%, the baseline score of 3 applies. The description does not enrich understanding of the parameters.
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 ('Generate an AI image') and identifies the specific resource ('Flux 2') with unique attributes (Black Forest Labs, photorealism). This distinguishes it from sibling image tools like ai_image_gpt or ai_image_pro by naming the exact model. The purpose is unambiguous.
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 implies usage through the 'photorealism' style and cost (~$0.08), but it does not explicitly state when to choose this tool over siblings or provide exclusion criteria. There is no mention of alternatives or use cases, leaving guidance implicit rather than explicit.
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.