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"Set a YepCode environment variable to be available for future code executions",
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inputSchema: zodToJsonSchema(SetEnvVarSchema),
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},
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{
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name: "remove_env_var",
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title: "Remove environment variable",
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description: "Remove a YepCode environment variable",
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inputSchema: zodToJsonSchema(RemoveEnvVarSchema),
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},
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{
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name: "get_execution",
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title: "Get process execution",
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description:
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"Get the status, result, logs, timeline, etc. of a YepCode execution",
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inputSchema: zodToJsonSchema(GetExecutionSchema),
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},
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...envVarsToolDefinitions,
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...storageToolDefinitions,
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...getExecutionToolDefinitions,
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];
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if(!this.disableRunCodeTool){
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constenvVars=awaitthis.yepCodeEnv.getEnvVars();
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constcodingRules=awaitthis.getCodingRules();
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tools.push({
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name: "run_code",
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title:
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"Execute LLM-generated code in YepCode’s remote and secure sandboxes",
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description: `This tool is ideal when your AI agent needs to handle tasks that don’t have a predefined tool available — but could be solved by writing and running a custom script.
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It supports JavaScript and Python, both with external dependencies (NPM or PyPI), so it’s perfect for:
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* Complex data transformations
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* API calls to services not yet integrated
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* Custom logic implementations
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* One-off utility scripts
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* To use files as input, first upload them to YepCode Storage using the upload storage MCP tools. Then, access them in your code using the \`yepcode.storage\` helper methods to download the files.
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* To generate and output files, create them in the local execution storage, then upload them to YepCode Storage using the \`yepcode.storage\` helpers. Once uploaded, you can download them using the download storage MCP tool.
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Tip: First try to find a tool that matches your task, but if not available, try generating the code and running it here.`,
"The process version to be executed. You may provide a specific version if user asks explicity for a process version."
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),
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comment: z
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.string()
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.optional()
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.describe(
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"A comment to be added to the execution. You may provide some context about the execution."
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),
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})
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.optional(),
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synchronousExecution: z
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.boolean()
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.optional()
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.default(true)
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.describe(
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"Whether the execution should be synchronous or not. If true, the execution will be synchronous and the execution result will be returned immediately. If false, the execution will be asynchronous and you should use the execution id to get the result later."
"Execute LLM-generated code in YepCode’s remote and secure sandboxes",
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description: `This tool is ideal when your AI agent needs to handle tasks that don’t have a predefined tool available — but could be solved by writing and running a custom script.
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+
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It supports JavaScript and Python, both with external dependencies (NPM or PyPI), so it’s perfect for:
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* Complex data transformations
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* API calls to services not yet integrated
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* Custom logic implementations
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* One-off utility scripts
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* To use files as input, first upload them to YepCode Storage using the upload storage MCP tools. Then, access them in your code using the \`yepcode.storage\` helper methods to download the files.
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* To generate and output files, create them in the local execution storage, then upload them to YepCode Storage using the \`yepcode.storage\` helpers. Once uploaded, you can download them using the download storage MCP tool.
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Tip: First try to find a tool that matches your task, but if not available, try generating the code and running it here.`,
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