Skip to main content
Glama

stop_instance

Stop a running Scrapybara instance by providing its instance ID. This tool halts virtual Ubuntu desktop operations when they are no longer needed.

Instructions

Stop a running Scrapybara instance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instance_idYesThe ID of the instance to stop.

Implementation Reference

  • Handler for the 'stop_instance' tool. Parses input arguments using StopInstanceSchema, retrieves the Scrapybara instance by ID, stops it, and returns the response as JSON.
    case "stop_instance": {
      const args = StopInstanceSchema.parse(request.params.arguments);
      const instance = await client.get(args.instance_id, {
        abortSignal: currentController.signal,
      });
    
      const response = await instance.stop({
        abortSignal: currentController.signal,
      });
    
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(response, null, 2),
          } as TextContent,
        ],
      };
    }
  • Zod schema defining the input for stop_instance tool: requires 'instance_id' as a string.
    export const StopInstanceSchema = z.object({
      instance_id: z.string().describe("The ID of the instance to stop."),
    });
  • src/index.ts:83-87 (registration)
    Registration of the 'stop_instance' tool in the ListTools response, including name, description, and input schema.
    {
      name: "stop_instance",
      description: "Stop a running Scrapybara instance.",
      inputSchema: zodToJsonSchema(StopInstanceSchema),
    },
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Stop') but lacks details on effects (e.g., whether data is preserved, permissions required, or if the operation is reversible). This is a significant gap for a mutation tool with zero annotation coverage.

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 a single, efficient sentence with zero waste, clearly front-loading the core action. It's appropriately sized for a simple tool with one parameter, earning its place without unnecessary elaboration.

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

Completeness2/5

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

Given the tool's complexity as a mutation operation with no annotations and no output schema, the description is incomplete. It lacks information on behavioral outcomes, error conditions, or return values, which is inadequate for guiding an agent in safe and effective use.

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%, with the single parameter 'instance_id' documented in the schema. The description adds no additional parameter details beyond what the schema provides, so it meets the baseline for high schema coverage without compensating value.

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 clearly states the action ('Stop') and target resource ('a running Scrapybara instance'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'start_instance' or 'get_instances' beyond the obvious verb difference, missing explicit comparison.

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 is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., the instance must be running), exclusions, or comparisons to siblings like 'start_instance' or 'act', leaving usage context implied but unspecified.

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/Scrapybara/scrapybara-mcp'

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