Skip to main content
Glama
Buggy1111

shelly-mcp

by Buggy1111

shelly_scene_run

Execute a named Shelly scene sequentially, applying all actions even if some fail. Returns per-action results and overall status for audit logging.

Instructions

Run a named scene: attempt every action in order, report per-action results.

Best-effort and sequential — a failing action does not abort the rest. status is ok (all succeeded), partial (some failed), or failed (none succeeded). A partial run can simply be re-run later to finish it (scenes use absolute states, so re-running is safe). Every action is audit-logged.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations are provided, so the description carries full burden. It discloses best-effort execution, sequential action attempt, non-aborting on failure, possible statuses ('ok', 'partial', 'failed'), re-run safety due to absolute states, and audit logging. This is comprehensive behavioral disclosure.

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 efficiently front-loaded with the core action. It uses 6 sentences, each adding value (behavior, status codes, re-running, audit logging). Slightly verbose but still clear and without redundancy.

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

Completeness5/5

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

Given the tool's simplicity (one parameter) and the presence of an output schema, the description covers behavioral expectations, error handling, and idempotency. No additional information is necessary for an agent to use this tool correctly.

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?

There is one parameter 'name' with no schema description. The description compensates by referring to 'named scene', indicating that the parameter identifies which scene to run. Although it doesn't specify constraints (e.g., valid values), the simple nature of the parameter and context make it clear.

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 runs a named scene, attempting each action in order, and reporting per-action results. It uses a specific verb 'Run' and identifies the resource as a 'named scene'. It distinguishes from sibling tools like shelly_scene_list and shelly_scene_get which list or retrieve scenes, not execute them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context that the tool is for executing scenes, and explains the best-effort, sequential behavior. It implies the correct use case (running a scene) but does not explicitly exclude alternatives. However, given sibling tool names, the purpose is unambiguous.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Buggy1111/shelly-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server