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Yes—you can build a coding assistant that reads files, proposes patches, and runs approved tests with Claude on Amazon Bedrock. The safe pattern is client-side tool use through Bedrock’s Converse API: Claude proposes a structured tool call, your Node.js application validates and executes it, then sends the result back with the exact tool-use ID. Claude never receives direct access to your repository or shell.
This guide shows the complete loop with the AWS SDK for JavaScript v3, narrowly scoped repository tools, validation, approval gates, error handling, and production limits. It also explains when Converse is preferable to Anthropic’s provider-specific request format.
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What does a Bedrock Claude coding assistant actually do?
A coding assistant is an orchestration service around a language model, not a model with unrestricted computer access. You provide Claude with a coding request, a system prompt, and descriptions of tools such as list_files, read_file, apply_patch, and run_tests. Claude decides whether one of those tools would help. Your Node.js process then checks the proposed arguments, performs the operation inside an allowed repository, and returns a structured result.
In Amazon Bedrock’s normal Converse flow, the model does not directly call your local functions. The application owns execution, permissions, timeouts, logging, and every side effect. This separation lets you keep secrets and operating-system access outside the model while still supporting multi-step tasks such as “find the failing test, edit the implementation, and run the focused test.”
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The request-and-result cycle
- The user sends a coding request to your Node.js service.
- Your service sends a Converse request containing the conversation, system instructions, model ID, inference settings, and tool definitions.
- Claude returns an assistant message. If it needs a tool, the response contains a structured
toolUseblock and a tool-use stop reason. - Your service validates the tool name and JSON input, applies repository and policy checks, then executes the matching function.
- Your service appends Claude’s complete assistant message to the conversation and sends a user message containing one or more
toolResultblocks. Each result must use the exacttoolUseIdClaude supplied. - Converse is called again. The loop ends when Claude returns ordinary text without a pending tool request, or when your round limit is reached.
What do I need before writing the Node.js code?
You need an AWS account with permission to invoke the selected Bedrock model, a region in which that model is available, Node.js, and a repository directory that the service is allowed to access. Keep the model ID and region in configuration so changing models does not require rewriting the orchestration loop.
Prerequisites
- Node.js 18 or a later LTS release, with ES modules enabled if you use
importsyntax. - An AWS Region where your chosen Claude model supports the Bedrock Converse API and tool use. Availability and compatibility vary by model, so verify both before deployment.
- AWS credentials supplied through the normal credential chain: environment variables, an AWS profile, an attached instance or task role, or another supported provider. Do not place long-lived keys in source code.
- A repository path that the service can read and, only when explicitly allowed, modify.
- Installed project dependencies and a test command you are prepared to run in a sandbox.
Install the SDK
npm install @aws-sdk/client-bedrock-runtimeThe JavaScript v3 entry points used here are BedrockRuntimeClient and ConverseCommand. The SDK handles signing and transport; your code still implements the tool protocol and all local operations.
Choose configuration values
const region = process.env.AWS_REGION || 'us-east-1';
const modelId = process.env.BEDROCK_MODEL_ID;
const repositoryRoot = process.env.REPOSITORY_ROOT;
if (!modelId || !repositoryRoot) {
throw new Error('BEDROCK_MODEL_ID and REPOSITORY_ROOT are required');
}Do not assume that a model ID available in one region is available in another. At startup, fail clearly if the configuration is missing instead of silently selecting a different model.
How should I design repository tools for Claude?
Start with several small, predictable operations rather than an unrestricted shell tool. A narrow schema gives Claude fewer ways to make an unsafe request and makes validation and auditing straightforward. The model can only choose tools you include in toolConfig; it cannot invent a new executable operation.
Recommended first tools
| Tool | Purpose | Important input controls | Typical policy |
|---|---|---|---|
list_files |
Show files beneath a relative directory | Relative path, depth limit, hidden-file policy | Read-only; cap number of entries |
read_file |
Return source text or a line range | Relative path, optional start and end lines, byte limit | Read-only; reject binary and oversized files |
apply_patch |
Apply a supplied patch to a named file | Relative target path, patch text, maximum changed lines | Require user confirmation before writing |
run_tests |
Run a predefined test profile | Allow-listed profile name, timeout | No arbitrary command text; sandbox and cap output |
A tool definition needs a stable name, a useful description, and a JSON input schema. Describe constraints in the schema and again in the implementation; schema text helps Claude, while code enforcement protects you when the model or a caller sends malformed input.
Tool configuration example
const toolConfig = {
tools: [
{
toolSpec: {
name: 'list_files',
description: 'List files under a repository-relative directory. Never returns file contents.',
inputSchema: {
json: {
type: 'object',
properties: {
path: { type: 'string', description: 'Repository-relative directory; use . for the root.' },
maxDepth: { type: 'integer', minimum: 0, maximum: 4 }
},
required: ['path']
}
}
}
},
{
toolSpec: {
name: 'read_file',
description: 'Read UTF-8 text from a repository-relative file, optionally by line range.',
inputSchema: {
json: {
type: 'object',
properties: {
path: { type: 'string' },
startLine: { type: 'integer', minimum: 1 },
endLine: { type: 'integer', minimum: 1 }
},
required: ['path']
}
}
}
},
{
toolSpec: {
name: 'apply_patch',
description: 'Apply a patch to one repository-relative file after the user has approved the change.',
inputSchema: {
json: {
type: 'object',
properties: {
path: { type: 'string' },
patch: { type: 'string' }
},
required: ['path', 'patch']
}
}
}
},
{
toolSpec: {
name: 'run_tests',
description: 'Run one predefined test profile in the repository sandbox.',
inputSchema: {
json: {
type: 'object',
properties: {
profile: { type: 'string', enum: ['unit', 'lint'] }
},
required: ['profile']
}
}
}
}
]
};Use the exact field casing expected by the installed SDK version. Bedrock normalizes the conversation interface, but model-specific support for tools and streaming still needs to be checked for the model you select.
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The key implementation detail is message history. Add Claude’s entire assistant message to messages before adding the user message that contains tool results. Omitting the assistant tool-use message, or changing its tool-use ID, breaks the protocol.
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Minimal working orchestration
import { BedrockRuntimeClient, ConverseCommand } from '@aws-sdk/client-bedrock-runtime';
const client = new BedrockRuntimeClient({ region });
const MAX_ROUNDS = 8;
const system = [
{
text: [
'You are a repository coding assistant.',
'Use tools only when needed. Explain intended edits before applying them.',
'Never assume a path is safe; stay inside the supplied repository.',
'Do not request secrets, credentials, or unrestricted shell access.'
].join(' ')
}
];
export async function assist(request) {
const messages = [
{ role: 'user', content: [{ text: request }] }
];
for (let round = 0; round < MAX_ROUNDS; round += 1) {
const response = await client.send(new ConverseCommand({
modelId,
messages,
system,
toolConfig,
inferenceConfig: {
temperature: 0.2,
maxTokens: 4096
}
}));
const assistantMessage = response.output?.message;
if (!assistantMessage) {
throw new Error('Bedrock returned no assistant message');
}
messages.push(assistantMessage);
if (response.stopReason !== 'tool_use') {
return {
text: assistantMessage.content
?.filter(block => block.text)
.map(block => block.text)
.join('') || '',
usage: response.usage,
metrics: response.metrics
};
}
const results = [];
for (const block of assistantMessage.content ?? []) {
if (!block.toolUse) continue;
const { name, input, toolUseId } = block.toolUse;
let output;
try {
const checked = validateToolInput(name, input);
output = await dispatch(name, checked);
} catch (error) {
output = {
ok: false,
error: error instanceof Error ? error.message : 'Tool failed'
};
}
results.push({
toolResult: {
toolUseId,
content: [{ json: output }]
}
});
}
if (results.length === 0) {
throw new Error('Tool-use stop reason contained no tool requests');
}
messages.push({ role: 'user', content: results });
}
throw new Error('Tool-call round limit exceeded');
}The stop reason is the signal to continue. A normal final response can include text and a non-tool stop reason. Treat an unexpected response shape as an error rather than guessing what Claude intended. The exact response fields can differ with SDK releases, so pin and test the version you deploy.
Dispatch only known names
async function dispatch(name, input) {
switch (name) {
case 'list_files':
return listFiles(input);
case 'read_file':
return readFile(input);
case 'apply_patch':
return applyPatchAfterApproval(input);
case 'run_tests':
return runTests(input);
default:
throw new Error(`Unknown tool: ${name}`);
}
}Never evaluate a model-produced function name, import path, or JavaScript expression. An explicit switch or map of callable functions makes the execution surface auditable.
How do I validate paths and file contents safely?
Resolve every model-provided path against a fixed repository root and reject traversal before touching the filesystem. Validation should happen after JSON parsing and before dispatch, even if the model’s schema says the value is a string.
import path from 'node:path';
import fs from 'node:fs/promises';
function safePath(relativePath) {
if (typeof relativePath !== 'string' || relativePath.length === 0) {
throw new Error('A non-empty relative path is required');
}
if (path.isAbsolute(relativePath)) {
throw new Error('Absolute paths are not allowed');
}
const root = path.resolve(repositoryRoot);
const candidate = path.resolve(root, relativePath);
const relative = path.relative(root, candidate);
if (relative.startsWith('..' + path.sep) || relative === '..' || path.isAbsolute(relative)) {
throw new Error('Path escapes the repository root');
}
return candidate;
}
async function readFile({ path: relativePath, startLine, endLine }) {
const filename = safePath(relativePath);
const stat = await fs.stat(filename);
if (!stat.isFile() || stat.size > 512 * 1024) {
throw new Error('File is missing, not regular, or larger than 512 KiB');
}
const text = await fs.readFile(filename, 'utf8');
const lines = text.split(/r?n/);
const from = startLine ? startLine - 1 : 0;
const to = endLine ? Math.min(endLine, lines.length) : lines.length;
if (from < 0 || from >= lines.length || to < from) {
throw new Error('Invalid line range');
}
return { ok: true, path: relativePath, content: lines.slice(from, to).join('n') };
}Consider symbolic links as part of this threat model. If the repository may contain links, verify the real path of the target remains inside the root, or reject links altogether. Cap returned bytes and directory entries so a request for a generated directory cannot exhaust memory or the model context.
How should patching and test execution be approved?
Reading is usually low risk; writing, installing packages, deleting files, and running commands can change state or access the network. Put a human or policy gate between Claude’s proposal and those operations.
Patch workflow
- Have Claude explain the intended change and produce a patch targeting one or more explicitly named files.
- Validate that every target path is inside the repository and that the patch size is below your limit.
- Show the diff to the user and request confirmation. Do not treat a model statement such as “approved” as user approval.
- Apply the patch in a temporary worktree or snapshot when possible.
- Return the resulting diff, changed-file list, and any failure details as structured JSON.
A patch tool should reject binary files, path headers that escape the root, and unexpectedly large additions or deletions. If a patch fails, return an actionable error to Claude; do not silently retry with a broader operation.
Allow-listed tests instead of arbitrary shell
const testCommands = {
unit: ['npm', ['test', '--', '--runInBand']],
lint: ['npm', ['run', 'lint']]
};
async function runTests({ profile }) {
const command = testCommands[profile];
if (!command) throw new Error('Unknown test profile');
return executeInSandbox(command[0], command[1], {
timeoutMs: 120000,
maxOutputBytes: 200000,
network: false
});
}The example intentionally does not accept a free-form command string. In production, execute in a container, temporary worktree, or other isolation boundary with a non-privileged user, a CPU and memory limit, a timeout, and a disabled or tightly controlled network. Package installation and network access should be separate, explicitly approved capabilities.
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What safety limits belong around the tool loop?
Model-generated arguments are untrusted input. A secure assistant limits both each operation and the overall conversation.
- Repository boundary: resolve paths against one root and reject traversal, absolute paths, and unsafe symlinks.
- Capability boundary: expose separate read, write, and execute tools; do not combine them into an unrestricted shell function.
- Approval boundary: require confirmation for deletion, broad rewrites, package installation, network requests, and commands that can alter data.
- Resource boundary: cap file size, directory entries, patch lines, process runtime, output bytes, concurrent calls, and total tool rounds.
- Secret boundary: redact environment variables and credentials from prompts, tool output, logs, and error messages.
- Audit boundary: record the tool name, validated arguments, start and end time, duration, and success status without recording secrets.
- Failure boundary: return structured errors that explain what failed and how to correct it; never convert an error into a successful-looking result.
Set a maximum round count even for apparently harmless tools. Without one, a model can repeatedly list, read, or test until latency and token costs become unacceptable. If the limit is reached, preserve the conversation and report that the assistant needs a new request.
How do I make the assistant’s prompts reliable?
The system prompt should define the repository boundary, the order of operations, and what requires approval. Ask Claude to inspect before editing, to use the smallest applicable patch, and to summarize tests honestly.
const system = [{ text: [
'You are an AI coding assistant operating through explicitly defined repository tools.',
'Use repository-relative paths only and inspect relevant files before proposing edits.',
'Never invent tool results, test outcomes, or files.',
'Do not request secrets or unrestricted commands.',
'Before any write or test operation, state the intended action and wait for the application approval signal.',
'When a tool fails, explain the error and choose a safer alternative.'
].join(' ') }];Prompt instructions are not a security control by themselves. The dispatcher and policy layer must enforce the same rules because a model can misunderstand, omit, or contradict a prompt.
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Claude may return several tool-use blocks in one assistant message. Execute only calls that pass validation and return one toolResult block per request. Preserve the order and exact IDs; if your product requires serial execution because operations conflict, reject or queue calls rather than running them concurrently.
Use a consistent result shape, for example { ok: true, data: ... } for success and { ok: false, error: ... } for failure. Keep errors concise enough for the model to act on, but include the rejected field or policy where useful. Do not include stack traces, credentials, or host-specific paths that reveal sensitive information.
Transport failures from Bedrock are different from tool failures. Retry transient AWS request errors according to your normal SDK policy, but do not repeat a destructive tool automatically. If a response is malformed, stop the loop and surface an operational error rather than attempting to infer missing IDs.
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Should I use Converse or Claude’s provider-specific Messages format?
Use Converse when you want a consistent Bedrock interface across message-capable models and are willing to work with normalized fields. Use a provider-specific Claude request format when you need Anthropic-native fields or syntax and accept tighter coupling to that provider’s contract.
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| Decision point | Converse API | Provider-specific Claude format |
|---|---|---|
| Portability | One Bedrock abstraction for supported message models | Optimized for Claude’s native contract |
| Response fields | Normalized output message, stop reason, usage, and metrics | Provider-native field names and semantics |
| Tool schema | Bedrock tool configuration and JSON schemas | Anthropic-specific tool syntax |
| Streaming | Check selected model and SDK support | Use the provider’s documented streaming behavior |
| Orchestration | Shared loop can support several Bedrock models | Potentially less translation for Claude-only features |
| Compatibility work | Still verify model tool support and field casing | Still verify the deployed Claude version and limits |
Converse does not remove the need to check model compatibility. Tool use, streaming, context limits, and inference settings can differ by model and region. Keep the model ID configurable and test the exact deployment combination.
How do I test the assistant before exposing it to users?
Test the orchestration and the tools independently. Mock the Bedrock client for deterministic protocol tests, then run a smaller number of end-to-end tests against the selected model in a disposable repository.
Protocol tests
- Claude returns a tool-use block, and your code appends the assistant message before the tool result.
- The returned
toolUseIdis copied byte-for-byte into the corresponding result. - Several tool-use blocks produce the correct number of results.
- A normal text response exits without another Converse call.
- A missing message, unknown tool, malformed JSON, or empty tool-use response fails closed.
- The maximum round count stops an intentional loop.
Security tests
- Paths such as
../secrets.txt, absolute paths, and symlink escapes are rejected. - Oversized files, directories, patches, and process output are capped.
- Unapproved write, delete, install, and network actions are blocked.
- Environment variables and tokens do not appear in tool results or logs.
- Commands outside the allow-list cannot be selected by changing a profile string.
Behavior tests
Use representative tasks: locate a function, explain a failing test, make a one-file change, and recover from a failed patch. Check that the assistant cites the files it actually read, distinguishes proposed changes from applied changes, and reports test failures instead of claiming success.
What commonly breaks, and how do I fix it?
“The tool result is rejected”
Check that the assistant tool-use message is present in the history and that the result uses the exact toolUseId. Also verify that the result is inside a user message with the expected content structure, not appended as plain text.
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“Claude keeps requesting tools”
Inspect whether your dispatcher is returning useful, bounded output. A silent failure or an empty success can cause repeated attempts. Add a round limit, return a clear error, and tell the model which prerequisite failed.
“The model never uses my tool”
Confirm the tool is included in the same request as toolConfig, its description explains when it is useful, and the selected model supports tool use through Converse. A vague description or an overly restrictive schema can make a valid tool look unusable.
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Replace free-form command input with named profiles, execute in an isolated environment, disable network access where possible, and require approval for profiles that can modify the repository or install dependencies.
What should a production deployment checklist contain?
- Pin and regularly update the AWS SDK version; run protocol tests after upgrades.
- Store the model ID, region, repository root, round limit, and resource caps in deployment configuration.
- Use an IAM role with only the Bedrock permissions the service needs; keep repository access separate from cloud credentials.
- Keep the service process non-privileged and isolate test execution.
- Implement request authentication, per-user quotas, cancellation, and timeouts around the API endpoint.
- Record latency, token usage returned by Converse, tool counts, and failure rates while redacting sensitive content.
- Provide a visible diff and approval history for every write operation.
- Make cancellation stop queued tools and terminate running test processes where supported.
- Review model and region compatibility before changing the configured model.
With these controls, the assistant remains a supervised repository agent: Claude supplies reasoning and structured intent, while your Node.js service retains ownership of code access and execution.
Frequently Asked Questions
Can Claude on Bedrock directly access my local repository?
No. In the standard Converse tool-use flow, Claude can only propose calls to the tools you describe. Your application must read files or run operations and then return the results.
Do I need a separate Bedrock agent to support function calling?
No. This pattern uses the Bedrock Runtime Converse API directly, with your Node.js process acting as the tool dispatcher. A separate agent service is optional and changes the architecture.
Can I stream Claude’s answer while tools are running?
Streaming and tool-event behavior depend on the selected model and SDK support. Design the non-streaming loop first, then verify streaming event handling for the exact model and region before enabling it.
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There is no universal safe number. Choose a limit based on task complexity and latency, enforce it in code, and return a clear limit-exceeded error so the user can continue with a new request.
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