mcp-server-circleci

Instructions

Implementation Reference

• src/tools/createPromptTemplate/handler.ts:12-46 (handler)

  The ToolCallback handler function that implements the core logic for the 'create_prompt_template' MCP tool. It invokes the CircletClient to generate a prompt template and returns a structured text response with next steps.
  export const createPromptTemplate: ToolCallback<{ params: typeof createPromptTemplateInputSchema; }> = async (args) => { const { prompt, promptOrigin, model } = args.params ?? {}; const circlet = new CircletClient(); const promptObject = await circlet.circlet.createPromptTemplate( prompt, promptOrigin, ); return { content: [ { type: 'text', text: `${promptOriginKey}: ${promptOrigin} ${promptTemplateKey}: ${promptObject.template} ${contextSchemaKey}: ${JSON.stringify(promptObject.contextSchema, null, 2)} ${modelKey}: ${model} NEXT STEP: - Immediately call the \`${PromptWorkbenchToolName.recommend_prompt_template_tests}\` tool with: - template: the \`${promptTemplateKey}\` above - ${contextSchemaKey}: the \`${contextSchemaKey}\` above - ${promptOriginKey}: the \`${promptOriginKey}\` above - ${modelKey}: the \`${modelKey}\` above - ${temperatureKey}: the \`${temperatureKey}\` above `, }, ], }; };
• src/tools/createPromptTemplate/inputSchema.ts:8-31 (schema)

  Zod schema defining the input parameters (prompt, promptOrigin, model, temperature) for the create_prompt_template tool.
  export const createPromptTemplateInputSchema = z.object({ prompt: z .string() .describe( "The user's application, feature, or product requirements that will be used to generate a prompt template. Alternatively, a pre-existing prompt or prompt template can be provided if a user wants to test, evaluate, or modify it. (Can be a multi-line string.)", ), promptOrigin: z .nativeEnum(PromptOrigin) .describe( `The origin of the prompt - either "${PromptOrigin.codebase}" for existing prompts from the codebase, or "${PromptOrigin.requirements}" for new prompts from requirements.`, ), model: z .string() .default(defaultModel) .describe( `The model that the prompt template will be tested against. Explicitly specify the model if it can be inferred from the codebase. Otherwise, defaults to \`${defaultModel}\`.`, ), temperature: z .number() .default(defaultTemperature) .describe( `The temperature of the prompt template. Explicitly specify the temperature if it can be inferred from the codebase. Otherwise, defaults to ${defaultTemperature}.`, ), });
• src/tools/createPromptTemplate/tool.ts:22-73 (registration)

  Defines the tool registration object with name 'create_prompt_template', detailed description, and references the input schema.
  export const createPromptTemplateTool = { name: PromptWorkbenchToolName.create_prompt_template, description: ` ABOUT THIS TOOL: - This tool is part of a toolchain that generates and provides test cases for a prompt template. - This tool helps an AI assistant to generate a prompt template based on one of the following: 1. feature requirements defined by a user - in which case the tool will generate a new prompt template based on the feature requirements. 2. a pre-existing prompt or prompt template that a user wants to test, evaluate, or modify - in which case the tool will convert it into a more structured and testable prompt template while leaving the original prompt language relatively unchanged. - This tool will return a structured prompt template (e.g. \`${templateKey}\`) along with a context schema (e.g. \`${contextSchemaKey}\`) that defines the expected input parameters for the prompt template. - In some cases, a user will want to add test coverage for ALL of the prompts in a given application. In these cases, the AI agent should use this tool to generate a prompt template for each prompt in the application, and should check the entire application for AI prompts that are not already covered by a prompt template in the \`${promptsOutputDirectory}\` directory. WHEN SHOULD THIS TOOL BE TRIGGERED? - This tool should be triggered whenever the user provides requirements for a new AI-enabled application or a new AI-enabled feature of an existing application (i.e. one that requires a prompt request to an LLM or any AI model). - This tool should also be triggered if the user provides a pre-existing prompt or prompt template from their codebase that they want to test, evaluate, or modify. - This tool should be triggered even if there are pre-existing files in the \`${promptsOutputDirectory}\` directory with the \`${fileNameTemplate}\` convention (e.g. \`${fileNameExample1}\`, \`${fileNameExample2}\`, \`${fileNameExample3}\`, etc.). Similar files should NEVER be generated directly by the AI agent. Instead, the AI agent should use this tool to first generate a new prompt template. PARAMETERS: - ${paramsKey}: object - ${promptKey}: string (the feature requirements or pre-existing prompt/prompt template that will be used to generate a prompt template. Can be a multi-line string.) - ${promptOriginKey}: "${PromptOrigin.codebase}" | "${PromptOrigin.requirements}" (indicates whether the prompt comes from an existing codebase or from new requirements) - ${modelKey}: string (the model that the prompt template will be tested against. Explicitly specify the model if it can be inferred from the codebase. Otherwise, defaults to \`${defaultModel}\`.) - ${temperatureKey}: number (the temperature of the prompt template. Explicitly specify the temperature if it can be inferred from the codebase. Otherwise, defaults to ${defaultTemperature}.) EXAMPLE USAGE (from new requirements): { "${paramsKey}": { "${promptKey}": "Create an app that takes any topic and an age (in years), then renders a 1-minute bedtime story for a person of that age.", "${promptOriginKey}": "${PromptOrigin.requirements}" "${modelKey}": "${defaultModel}" "${temperatureKey}": 1.0 } } EXAMPLE USAGE (from pre-existing prompt/prompt template in codebase): { "${paramsKey}": { "${promptKey}": "The user wants a bedtime story about {{topic}} for a person of age {{age}} years old. Please craft a captivating tale that captivates their imagination and provides a delightful bedtime experience.", "${promptOriginKey}": "${PromptOrigin.codebase}" "${modelKey}": "claude-3-5-sonnet-latest" "${temperatureKey}": 0.7 } } TOOL OUTPUT INSTRUCTIONS: - The tool will return... - a \`${templateKey}\` that reformulates the user's prompt into a more structured format. - a \`${contextSchemaKey}\` that defines the expected input parameters for the template. - a \`${promptOriginKey}\` that indicates whether the prompt comes from an existing prompt or prompt template in the user's codebase or from new requirements. - The tool output -- the \`${templateKey}\`, \`${contextSchemaKey}\`, and \`${promptOriginKey}\` -- will also be used as input to the \`${PromptWorkbenchToolName.recommend_prompt_template_tests}\` tool to generate a list of recommended tests that can be used to test the prompt template. `, inputSchema: createPromptTemplateInputSchema, };
• src/circleci-tools.ts:37-54 (registration)

  Registers the createPromptTemplateTool in the main CCI_TOOLS array for the MCP server.
  export const CCI_TOOLS = [ getBuildFailureLogsTool, getFlakyTestLogsTool, getLatestPipelineStatusTool, getJobTestResultsTool, configHelperTool, createPromptTemplateTool, recommendPromptTemplateTestsTool, runPipelineTool, listFollowedProjectsTool, runEvaluationTestsTool, rerunWorkflowTool, downloadUsageApiDataTool, findUnderusedResourceClassesTool, analyzeDiffTool, runRollbackPipelineTool, listComponentVersionsTool, ];
• src/clients/circlet/circlet.ts:28-46 (helper)

  Underlying API client method called by the tool handler to create the prompt template via HTTP POST to '/workbench'.
  async createPromptTemplate( prompt: string, promptOrigin: PromptOrigin, ): Promise<PromptObject> { const result = await this.client.post<WorkbenchResponse>('/workbench', { prompt, promptOrigin, }); const parsedResult = WorkbenchResponseSchema.safeParse(result); if (!parsedResult.success) { throw new Error( `Failed to parse workbench response. Error: ${parsedResult.error.message}`, ); } return parsedResult.data.workbench; }