tensorart-mcp

Overview Schema Source Related Servers Discussions Admin

image_to_image

Re-render an uploaded local image with a text prompt and optional LoRAs to produce edited variations, with configurable dimensions, steps, and denoising.

Instructions

Image-to-image: uploads a local image and re-renders it with the prompt (and LoRA).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "image_path": "<image_path>",
      "prompt": "<prompt>"
    },
    "name": "image_to_image"
  }
}

Input Schema

Name	Required	Default
`seed`	No
`wait`	No
`loras`	No
`steps`	No
`width`	No
`height`	No
`prompt`	Yes
`sampler`	No	euler
`model_id`	No
`cfg_scale`	No
`image_path`	Yes
`timeout_sec`	No
`negative_prompt`	No
`denoising_strength`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 2, 2026

TDQS

C2.7/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses that a local image is uploaded and re-rendered with a prompt and LoRA, but it does not explain job/async behavior despite the `wait` and `timeout_sec` parameters, nor does it mention credit consumption, output format, or what happens when `wait` is false.

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 a single front-loaded sentence with no filler. It is concise and readable, though its brevity leaves significant gaps for a 14-parameter tool; the conciseness itself is not the problem.

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

Completeness2/5

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

For a tool with 14 parameters, zero schema description coverage, no annotations, and no output schema, one sentence is not sufficient. The description gives the high-level operation but omits parameter semantics, async/job behavior, output characteristics, and practical guidance needed to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across 14 parameters. The description adds meaning for `image_path` (local upload), `prompt` (re-render instruction), and partially `loras`, but it leaves 11 parameters undocumented, including important controls like `denoising_strength`, `wait`, `seed`, `steps`, `width`, `height`, `sampler`, `cfg_scale`, and `timeout_sec`.

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 states a specific operation: uploading a local image and re-rendering it with a prompt and optional LoRA. It clearly identifies the image-to-image workflow and distinguishes itself conceptually from text-to-image generation, though it does not explicitly name the sibling generate_image as the alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when the user has a local image to re-render, but it provides no explicit when-to-use guidance, no exclusions, and no comparison to generate_image or run_workflow. An agent must infer that this tool is for image-conditioned generation rather than text-only generation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server