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Cursor does not necessarily send your entire repository to the model for every request. It estimates which parts are relevant, then combines that retrieved context with your prompt and any files, folders, or symbols you explicitly reference. For Lambda work, you can use Cursor as a development environment for an AWS Lambda application—or, in a separate advanced setup, run Cursor Cloud Agent tool calls on AWS Lambda MicroVMs.
How does Cursor understand your codebase?
Cursor describes context as information supplied to the model. Its automatic retrieval estimates which repository portions are useful for a request, such as the current file and semantically similar code patterns. This is not evidence that every request includes the whole repository: what is selected depends on the task and the model’s available context.
Cursor’s context guide distinguishes intent context—what you want to accomplish—from state context—the code, logs, and other details describing the project’s current condition. A clear request supplies intent; relevant files and examples help establish state. If important context is missing, suggestions may be less reliable, or an agent may work inefficiently. See Cursor’s context guide.
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When you already know where a behavior lives, add the relevant reference to your prompt instead of relying only on automatic retrieval:
@codefor a known symbol or function.@filefor a specific file.@folderfor a directory whose contents matter to the task.
For example, when changing a Lambda handler, reference the handler and any related configuration or tests that define its inputs and expected behavior. Asking Cursor to trace a request from its entry point through those known files makes the intended code path clearer than saying only “fix the Lambda.” Cursor documents these context references in its @ symbol reference.
Make recurring guidance reusable
Use Cursor rules to preserve project conventions and repeatable workflow guidance, such as how the project structures handlers, validates input, or runs tests. This avoids restating stable instructions in every prompt. Rules guide the assistant; they do not guarantee that every relevant implementation detail has been retrieved.
For information outside the repository, MCP can connect Cursor to external tools and data sources. That can make internal documentation or project-management information available alongside code context. Connections and permissions should be configured deliberately; MCP is a way to provide access, not proof that Cursor already knows those systems. See Cursor’s MCP documentation.
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Does Cursor read your whole repository?
Do not assume that a request places every file in the model’s context. Cursor describes retrieving portions it estimates are relevant, while also allowing you to specify context directly. For a change that crosses a handler, shared utility, infrastructure definition, and test, name or attach those pieces explicitly and ask Cursor to account for each one.
Indexing supports search across a project, but indexing and per-request model context are not the same thing. Cursor’s security documentation describes scanning an opened folder, honoring .gitignore and .cursorignore, and using a synced Merkle tree to identify changes. It also describes chunking and embedding files and storing obfuscated relative paths and line ranges for vector search. These are product implementation details and may change; consult Cursor’s security documentation for current behavior and exclusion controls.
What indexing means for privacy
Indexing should not be described as purely local. Cursor’s privacy page says chunks are uploaded for embedding; plaintext code ceases to exist after the embedding request, while embeddings and metadata are stored. The exact policy and available controls can change, so review the current Cursor privacy policy and settings before indexing sensitive repositories. Apply exclusions where appropriate and follow your organization’s rules for source code and external services.
How can you use Cursor to develop an AWS Lambda application?
For ordinary Lambda development, Cursor is the editor and AI assistant you use to work on the application. AWS announced on August 6, 2026 that the Lambda console can open a function in Cursor, preserving its existing code and configuration. AWS also describes using Cursor to convert applications to an AWS SAM template. The announcement says the workflow is available in commercial AWS Regions where Lambda is available and has no additional charge; check the current AWS announcement for regional availability and details: AWS Lambda’s Cursor integration announcement.
A practical development workflow
- Open the function in Cursor. Use the Lambda console’s Cursor option where it is available, or work with the project in Cursor through your normal development workflow.
- Give the task useful context. Reference the handler, relevant shared code, configuration, and tests with
@file,@folder, or@code. State the intended behavior and any constraints. - Ask for AWS-aware help when needed. AWS publishes instructions for adding its serverless skill and configuring the AWS Serverless MCP Server. These can provide AWS-specific guidance and tool access: AWS agent setup guide.
- Review and validate changes yourself. Inspect generated code, configuration, and any proposed infrastructure changes; run the project’s tests and use your established AWS deployment and verification process. Cursor assistance does not replace those controls.
AWS SAM is relevant when you want an application represented with a SAM template. The console integration supports conversion, but that does not mean every Lambda project must use SAM or that the conversion removes the need to review the resulting template.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is different about Cursor Cloud Agents on Lambda MicroVMs?
AWS documents a separate pattern in which Lambda MicroVMs act as self-hosted machines for Cursor Cloud Agents. This is not the same as opening a Lambda function in Cursor to edit its code. In the documented architecture, Cursor hosts the agent loop and model; a worker running in a Lambda MicroVM claims pool requests and executes tool calls in the customer’s AWS environment. A scheduled controller Lambda responds to pending requests.
AWS describes the MicroVMs as Firecracker-isolated, with sessions that do not share state. Each session runs for up to eight hours, after which the MicroVM is terminated. AWS summarizes the model: “You can use AWS Lambda MicroVMs as self-hosted machines for your Cursor Cloud Agents, keeping repositories, tool execution, and network access in infrastructure you control.” Read the AWS Lambda MicroVM guide for Cursor Cloud Agents for the architecture and implementation details.
Requirements for the self-hosted worker pattern
This is an advanced deployment, not a prerequisite for an individual developer editing Lambda code locally. AWS’s documented setup calls for:
- An AWS account with Lambda MicroVMs enabled, plus permissions for S3, IAM, CloudFormation, and Systems Manager Parameter Store.
- Cursor Enterprise with self-hosted machines enabled, and a service-account API key.
- A current AWS CLI and Docker.
The documented approach stores the API key in Systems Manager Parameter Store as a SecureString rather than baking it into the worker image. Follow AWS’s guide for the exact deployment steps and configuration; do not treat the worker pattern as a substitute for reviewing credentials, permissions, network access, or the code executed by an agent.
Which Lambda workflow fits your task?
| Question | Developing a Lambda application in Cursor | Cursor Cloud Agent workers on Lambda MicroVMs |
|---|---|---|
| What runs where? | You use Cursor to edit and assist with a Lambda application; AWS documents opening functions from the Lambda console and SAM conversion. | Cursor hosts the agent loop and model; tool calls run in a self-hosted worker on a Lambda MicroVM in your AWS environment. |
| Who is it for? | Developers working on Lambda code. | Teams deploying an advanced self-hosted Cloud Agent worker setup. |
| What additional setup is documented? | AWS serverless skill and Serverless MCP Server are optional AWS-specific assistance paths. | Lambda MicroVM access and AWS permissions, Cursor Enterprise with self-hosted machines enabled, a service-account API key, AWS CLI, and Docker. |
| What operational limit is stated? | The cited announcement does not state a session limit. | AWS documents sessions of up to eight hours per MicroVM. |
Choose the first workflow when your goal is to build or change a Lambda application with Cursor’s help. Consider the MicroVM pattern only when you specifically need Cloud Agent tool execution in self-hosted AWS infrastructure and can support its account, permissions, and operational setup.
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