tensorart-mcp

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generate_image

Generate text-to-image artwork on Tensor.Art from prompts using custom checkpoints, LoRAs, and diffusion settings, with optional wait-to-completion and local image saving.

Instructions

Text-to-image on Tensor.Art (TAMS).

model_id: checkpoint id (number from the model page URL). loras: list of {"id": "", "weight": 1.0, "access_key": ""}. model_access_key: for a private checkpoint. extra_diffusion: any additional DIFFUSION fields TAMS accepts (e.g. {"scheduleName": "simple", "guidance": 3.5}). With wait=True it polls until the job ends and saves the images locally.

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": {
      "prompt": "<prompt>"
    },
    "name": "generate_image"
  }
}

Input Schema

Name	Required	Default
`vae`	No	Automatic
`seed`	No
`wait`	No
`count`	No
`loras`	No
`steps`	No
`width`	No
`height`	No
`prompt`	Yes
`sampler`	No	euler
`download`	No
`model_id`	No
`cfg_scale`	No
`clip_skip`	No
`timeout_sec`	No
`extra_diffusion`	No
`negative_prompt`	No
`model_access_key`	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 wait=True polls until completion and saves images locally, but omits credit consumption, what happens with wait=False, how the job handle relates to get_job/cancel_job, authentication needs, and error behavior.

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 compact and front-loads the core purpose before parameter notes. The telegraphic lines are efficient, though the abbreviation 'TAMS' is used without definition and the parameter notes could be organized more clearly.

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 an 18-parameter generation tool with no annotations, no output schema, and no schema descriptions, the definition is under-specified. It explains only a few parameters and the wait behavior, leaving the agent without enough context on generation controls, job lifecycle, or return values.

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 18 parameters. The description adds useful format details for model_id, loras, model_access_key, extra_diffusion, and wait, but does not explain prompt, negative_prompt, width, height, steps, sampler, cfg_scale, seed, count, vae, download, clip_skip, or timeout_sec, leaving most parameters semantically bare.

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 first sentence clearly states a specific operation and resource: text-to-image generation on Tensor.Art. It does not explicitly differentiate from the sibling image_to_image tool, but the phrase 'text-to-image' itself makes the core purpose unambiguous.

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 never says when to use this instead of image_to_image, run_workflow, or run_workflow_template, nor does it mention prerequisites, credit costs, or the estimate_credits sibling. Usage is only implied by the tool name and purpose sentence.

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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