LogiSwarm AI MCP Server

inspect_cargo_multimodal

Read-onlyIdempotent

Analyze cargo damage or shipping labels using Google Gemini multimodal vision. Submit a base64 image to detect issues and verify label details for logistics inspections.

Instructions

Performs Google Gemini multimodal vision inspection on cargo damage or shipping labels.

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_base64": "<image_base64>"
    },
    "name": "inspect_cargo_multimodal"
  }
}

Input Schema

Name	Required	Description	Default
`cargo_id`	No	Consignment / Order ID
`image_base64`	Yes	Base64-encoded image of the package or cargo

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.0Oct 17, 2026

TDQS

B3.3/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true and destructiveHint=false, so the safety profile is covered. The description adds a genuinely non-obvious behavioral trait by naming Google Gemini multimodal inference, implying an external, potentially slow/paid model call, but says nothing about return content, failure modes, or rate/cost limits.

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

Conciseness5/5

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

A single front-loaded sentence naming the action, the model, and the subject matter, with no filler or redundancy. Nothing 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?

There is no output schema, so the description would ideally indicate what the inspection returns (e.g. a damage verdict or label reading). Annotations cover the safety profile and the schema covers inputs, but the return-value gap makes this only minimally complete.

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% for both parameters, so the baseline applies. The description narrows the semantic intent of the image ('cargo damage or shipping labels'), which slightly sharpens what image_base64 should contain, but adds no format, size, or encoding detail beyond the schema.

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?

States a specific verb ('Performs ... inspection') and resource ('cargo damage or shipping labels') on image input, which is clear enough for an agent to select it. It is the only inspection/vision tool among siblings like get_fleet_status and optimize_cvrp_routes, so differentiation is implicit rather than stated.

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?

No guidance on when to use this versus alternatives, no prerequisites (e.g. when an image is available), and no exclusions. The agent must infer usage entirely from the one-line purpose statement.

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

Deploy Server