Snipe-IT MCP Server

accessory_operations

Check out accessories to users, assets, or locations; check them back in and list current checkouts. Manage accessory inventory assignments and quantity tracking directly.

Instructions

Perform checkout/checkin operations on accessories.

Accessories can be checked out to a user, asset, or location. Each checkout decrements the available quantity, and checkin increments it back.

Operations:

checkout: Checkout an accessory to a user/asset/location (requires checkout_data with checkout_to_type and assigned_to_id)
checkin: Checkin an accessory (requires checkout_id from the checkout record)
list_checkouts: List all users who have this accessory checked out

Returns: dict: Result of the operation including success status and data

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": {
      "action": "<action>",
      "accessory_id": "<accessory_id>"
    },
    "name": "accessory_operations"
  }
}

Input Schema

Name	Required	Description
`action`	Yes	The operation to perform on the accessory
`checkout_id`	No	Checkout ID (required for checkin action)
`accessory_id`	Yes	Accessory ID
`checkout_data`	No	Checkout data (required for checkout action)

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.6.0Aug 2, 2026

TDQS

A4/5.0

Behavior4/5

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

The description discloses the inventory effect (checkout decrements quantity, checkin increments it back) and notes that checkin requires the checkout_id from a prior checkout. This goes beyond the annotations (which only say non-read-only, non-idempotent, non-destructive) and provides useful behavioral context.

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?

The description is well-structured and succinct: an introductory sentence, a bulleted list of operations with requirements, and a clear return type. Every sentence serves a purpose with no redundant fluff, and the most critical information is front-loaded.

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

Completeness4/5

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

For a tool with 4 parameters and 3 actions, the description covers the main operations, required parameters, and the quantity side effect. It also states a return type. It does not detail edge cases or list_checkouts output specifics, but given the schema and output schema exist, this is largely sufficient.

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?

The description reiterates the required parameters for each action (e.g., checkout requires checkout_data with checkout_to_type and assigned_to_id) and adds the nuance that checkout_id comes from the checkout record. Since the schema already provides 100% coverage with clear descriptions, the incremental value is modest, earning a baseline score.

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

Purpose5/5

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

The description clearly states it performs checkout/checkin operations on accessories and lists the three specific operations (checkout, checkin, list_checkouts) with their targets. This distinguishes it from sibling tools like asset_operations or component_operations by scope and action.

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 explains what each operation does and what parameters are required, but it does not explicitly contrast with alternatives or state when to use this tool over others. The context is clear from the name and sibling list, but no exclusions or alternative pointers are given.

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