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tensorart-mcp

tensorart-mcp

by giacomodiguida
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_model

Retrieve checkpoint or LoRA details by model ID to verify model type, base model, and usability before generating or rendering images.

Instructions

Info on a model (checkpoint or LoRA) by id: type, base model, whether it can be used.

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": {
      "model_id": "<model_id>"
    },
    "name": "get_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
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 does disclose the shape of the result (type, base model, usability) and implies a safe read, but says nothing about behavior for an unknown id, permission requirements, or whether LoRA and checkpoint ids share a namespace.

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?

A single front-loaded sentence with no filler; the parenthetical qualifies the resource efficiently. Slightly terse given the gaps elsewhere, but nothing in it is wasted.

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?

With no output schema and no annotations, the description should fully stand in for both. It partially does by enumerating returned fields, but leaves error behavior and the meaning of 'whether it can be used' unexplained, which is a meaningful gap for a lookup tool.

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 0%, so the description must compensate for the single undocumented parameter. It clarifies that model_id identifies either a checkpoint or a LoRA, which adds real meaning, but gives no format or id-convention detail.

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 names a specific resource (a model, checkpoint or LoRA) and a lookup action by id, and even previews the returned attributes (type, base model, usability). It is clear enough to separate from the job/image/workflow siblings, though it doesn't explicitly state it is the model counterpart to those tools.

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?

There is no statement of when to call this rather than another tool, nor any prerequisite or exclusion. The only implied usage is 'when you have a model id', which an agent must infer from the single required parameter.

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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