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ai_edit

Edit images using AI with natural language prompts. Supports single and multi-image editing for visual content modification.

Instructions

Edit images using AI with natural language prompts. Supports single and multi-image editing. Cost: $0.15. Requires Tier 1+.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
media_urlYesURL of the primary image to edit
promptYesNatural language instruction for editing
additional_imagesNoUp to 2 additional reference image URLs
aspect_ratioNoOutput aspect ratio (default: match input image)match_input_image
seedNoRandom seed for reproducible results

Implementation Reference

  • The `handle_tools_call` function acts as the central router for tool execution in the VAP MCP Proxy. While it currently explicitly handles `generate_video`, `estimate_video_cost`, and `get_task`, other tools (including potential future 'ai_edit' tools) are forwarded directly to the backend API via `make_request`.
    def handle_tools_call(params: Dict) -> Dict:
        """
        Handle tools/call request.
    
        Directive #240: Special handlers for video tools.
        """
        tool_name = params.get("name", "")
        arguments = params.get("arguments", {})
    
        # ═══════════════════════════════════════════════════════════════════
        # VIDEO TOOL HANDLERS (Directive #240)
        # ═══════════════════════════════════════════════════════════════════
    
        if tool_name == "generate_video":
            return _handle_generate_video(arguments)
    
        if tool_name == "estimate_video_cost":
            return _handle_estimate_video_cost(arguments)
    
        if tool_name == "get_task":
            return _handle_get_task(arguments)
    
        # Default: forward to MCP API
        response = make_request("/tools/call", {
            "name": tool_name,
            "arguments": arguments
        })
        return response
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses cost ('Cost: $0.15') and a prerequisite ('Requires Tier 1+'), which are useful behavioral traits. However, it lacks details on rate limits, error handling, or what the output looks like (since no output schema exists). This adds some value but is incomplete for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded with the core purpose. Every sentence adds value: the first states the action, the second adds capability details, and the third provides cost and prerequisites. It could be slightly more structured but is efficient with zero waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description is moderately complete. It covers purpose, cost, and prerequisites but lacks details on behavioral aspects like rate limits or output format. For an AI editing tool with 5 parameters and no structured output, it should do more to compensate for the missing annotations, making it adequate but with gaps.

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?

Schema description coverage is 100%, so the schema already documents all 5 parameters well. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain 'prompt' formats or 'additional_images' use cases). Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Edit images using AI with natural language prompts.' It specifies the resource (images) and the action (edit with AI/prompts). However, it does not explicitly differentiate from sibling tools like 'inpaint' or 'background_remove,' which might also involve image editing, so it lacks sibling differentiation for a perfect score.

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 description provides some usage context: 'Supports single and multi-image editing' and 'Requires Tier 1+.' It implies when to use this tool (for AI-based image editing with prompts) but does not explicitly state when not to use it or name alternatives among siblings (e.g., vs. 'inpaint' or 'background_remove'). This is adequate but has clear gaps in guidance.

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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