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Lexmata

Bitbucket Cloud MCP Server

by Lexmata

stop_pipeline

Stop a running Bitbucket Cloud pipeline by providing workspace, repository, and pipeline identifiers to halt execution.

Instructions

Stop a running pipeline.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workspaceYesThe workspace slug
repo_slugYesThe repository slug
pipeline_uuidYesThe pipeline UUID

Implementation Reference

  • Zod schema definition for stop_pipeline tool input validation.
    stop_pipeline: z.object({
      workspace: z.string().describe('The workspace slug'),
      repo_slug: z.string().describe('The repository slug'),
      pipeline_uuid: z.string().describe('The pipeline UUID'),
    }),
  • Tool registration in the toolDefinitions array with name, description, and input schema for MCP.
    {
      name: 'stop_pipeline',
      description: 'Stop a running pipeline.',
      inputSchema: {
        type: 'object' as const,
        properties: {
          workspace: { type: 'string', description: 'The workspace slug' },
          repo_slug: { type: 'string', description: 'The repository slug' },
          pipeline_uuid: { type: 'string', description: 'The pipeline UUID' },
        },
        required: ['workspace', 'repo_slug', 'pipeline_uuid'],
      },
    },
  • ToolHandler.handleTool case for stop_pipeline: validates args, delegates to PipelinesAPI.stop, returns success.
    case 'stop_pipeline': {
      const params = toolSchemas.stop_pipeline.parse(args);
      await this.pipelines.stop(params.workspace, params.repo_slug, params.pipeline_uuid);
      return { success: true, message: 'Pipeline stopped' };
    }
  • PipelinesAPI.stop: Posts to Bitbucket Pipelines API endpoint to stop the specified pipeline.
    async stop(workspace: string, repo_slug: string, pipeline_uuid: string): Promise<void> {
      await this.client.post(
        `/repositories/${workspace}/${repo_slug}/pipelines/${pipeline_uuid}/stopPipeline`
      );
    }
Behavior2/5

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

With no annotations provided, the description carries full burden but only states the action without behavioral details. It doesn't disclose effects (e.g., whether stopping is reversible, if it requires specific permissions, or what happens to pipeline artifacts), which is inadequate 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 that front-loads the core action without unnecessary words. Every part earns its place by directly conveying the tool's purpose, making it optimally concise.

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?

For a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, error conditions, or return values, leaving significant gaps that could hinder an agent's ability to use it correctly in complex scenarios.

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 schema description coverage is 100%, with all three parameters clearly documented in the schema. The description adds no additional meaning beyond implying the pipeline is identified by workspace, repo, and UUID, 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 verb ('Stop') and resource ('a running pipeline'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_pipeline' or 'trigger_pipeline' beyond the action itself, which prevents a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., the pipeline must be running), exclusions, or related tools like 'get_pipeline' for status checks, leaving the agent to infer usage from context alone.

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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