Lumorig

Give a mascot something to hold or ride

add_gear

Give the mascot a stock thing as a look of its own, with the motion that goes with it: notes (a notepad and pencil, motion 'notes'), book ('read'), laptop ('type'), stool (it sits on it), party (a hat, and confetti in 'celebrate'), sign ('sign'), magnifier ('search'), phone ('phone'), mug ('sip'), megaphone ('announce'), skateboard ('ride'). Drawn in the mascot's own outline and colours. Free and instant. A character whose arms can't reach is told why. 'none' puts it away.

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": {
      "id": "<id>",
      "gear": "<gear>"
    },
    "name": "add_gear"
  }
}

Input Schema

Name	Required	Description	Default
`id`	Yes
`gear`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 5, 2026

TDQS

A3.9/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does well: it states the artwork is 'Drawn in the mascot's own outline and colours', that the operation is 'Free and instant', and it discloses a failure mode ('A character whose arms can't reach is told why'). It does not describe the return payload, but the behavioral profile is otherwise clear.

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?

Purpose is front-loaded in the opening clause, followed by a compact enumeration and two short behavioral notes; the long gear list is dense but every item carries information the schema does not. There is no filler.

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 two-parameter tool with no output schema and no annotations, the description covers what gets added, the motion side effect, the removal value, cost/latency, and the one expected failure. The gap is the unexplained 'id' parameter and the absence of any return-value indication.

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

Parameters4/5

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

Schema coverage is 0%, so the description must compensate, and it does for 'gear' by mapping every enum value to its visual and its associated motion (e.g. party = hat with confetti in 'celebrate'). The 'id' parameter is never explained, so the agent must assume it is the mascot identifier.

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 gives a specific verb and resource ('Give the mascot a stock thing ... with the motion that goes with it') and enumerates the exact gear/motion pairs, so an agent knows precisely what is added. It does not distinguish itself from the sibling add_prop or set_props, which leaves some overlap ambiguity.

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?

Usage is implied through the enumerated gear list and the note that 'none' puts it away, which tells the agent how to remove gear. There is no explicit when-to-use versus add_prop/set_props guidance and no stated prerequisites, so the agent must infer the selection rules.

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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