infrawise

<p align="center">
  <a href="https://sidd27.github.io/infrawise/">
    <img src="https://raw.githubusercontent.com/Sidd27/infrawise/main/website/public/logo-400.png" alt="Infrawise logo" width="130" />
  </a>
</p>

<h1 align="center">Infrawise</h1>

<p align="center"><b>Your AI coding assistant finally knows your infra.</b></p>

<p align="center">
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</p>

<p align="center">
  <a href="https://sidd27.github.io/infrawise/">Website</a> ·
  <a href="https://sidd27.github.io/infrawise/getting-started/installation/">Docs</a> ·
  <a href="#quick-start">Quick start</a>
</p>

Infrawise gives AI coding assistants deterministic infrastructure awareness.

It statically analyzes your codebase, cloud infrastructure, and database schemas, then exposes that context through MCP so tools like Claude Code can understand your actual tables, indexes, query patterns, and service relationships instead of guessing from source files alone.

![infrawise start --claude, then Claude Code answers an SQS handler question with the exact event shape and queue risks pulled live from infrawise](https://raw.githubusercontent.com/Sidd27/infrawise/main/docs/demo/gifs/mcp.gif)

---

## Why this exists

New software developers don't write wrong code. Claude Code writes wrong code and they ship it. Infrawise is the only thing standing between Claude Code's generated output and a production incident.

AI coding assistants can read your source files but have no deterministic knowledge of your infrastructure. They do not know which GSIs exist, how tables are partitioned, which functions already trigger scans, or where indexes are missing. So they guess.

Infrawise replaces guessing with infrastructure-aware context.

**Without Infrawise**, an AI assistant might:

- Suggest a `.scan()` on your Orders table that has 50M rows
- Recommend adding a GSI on `status` that you already have
- Write a `SELECT *` when you need to keep query cost low
- Not notice that 5 functions are already hammering the same partition key

**With Infrawise**, it knows:

- Your exact table schemas, partition keys, sort keys, and GSIs
- Which functions query which tables and how
- Which patterns are already flagged as high severity
- The exact `CREATE INDEX` SQL or GSI config for your tables — not generic advice

---

## What Infrawise is not

Infrawise is not an AI agent framework, an infrastructure provisioning tool, an observability platform, or a cloud management dashboard.

It is a deterministic infrastructure intelligence layer for AI-assisted development.

---

## Installation

Requires **Node.js 22 or later** (`node --version`).

```bash
npm install -g infrawise
```

or use without installing:

```bash
npx infrawise start --claude
```

---

## Quick start

```bash
cd your-project
infrawise start --claude
```

That's it. Infrawise will:

1. Probe your environment and generate `infrawise.yaml` (first time only — asks which AWS profile to use only if you have several)
2. Scan your AWS services, databases, and codebase
3. Write `.mcp.json` so your editor auto-connects on every future launch
4. Open Claude Code with all 21 MCP tools ready

**Every time after:**

```bash
claude    # no infrawise command needed — editor manages the connection
```

Analysis is cached for 24 hours. When the cache is stale, `infrawise serve --stdio` (spawned automatically by your editor) refreshes it at session start. File changes are detected within the session and the code graph is updated automatically.

```
Findings (3 total)

1. [HIGH] Full table scan detected on DynamoDB table "Orders"
   listAllOrders() scans without any filter — reads every item in the table.
   Recommendation: Replace Scan with Query using a partition key or add a GSI.

2. [MEDIUM] PostgreSQL table "users" has no index on column "email"
   Filtering on "email" causes sequential scans.
   Recommendation: CREATE INDEX CONCURRENTLY idx_users_email ON users(email);

3. [MEDIUM] DynamoDB table "Sessions" accessed by 6 distinct code paths
   High access concentration may create hot partition issues at scale.
```

---

## Using with AI coding assistants

### Claude Code (recommended)

```bash
infrawise start --claude
```

Writes `.mcp.json` to your project root (merging with any MCP servers already configured there) and opens Claude Code. Claude Code reads `.mcp.json` automatically on every launch and manages the `infrawise serve --stdio` process — no server to start, no ports to configure.

### Cursor

```bash
infrawise start --cursor
```

Writes `.cursor/mcp.json` (merging with any existing MCP servers) and opens Cursor. All 21 infrawise tools are available in Cursor's MCP panel.

### VS Code

```bash
infrawise start --vscode
```

Writes `.vscode/mcp.json` (merging with any existing MCP servers) and opens VS Code. The tools are available to Copilot agent mode via the MCP servers panel.

### Any editor (no flag)

```bash
infrawise start
```

Writes `.mcp.json` (merging with any existing MCP servers) and exits. Open whichever editor you prefer — point it at `infrawise serve --stdio --config /path/to/infrawise.yaml` as an MCP server command.

### HTTP transport (alternative)

If your editor or workflow requires an HTTP MCP endpoint instead of stdio:

```bash
infrawise serve    # starts server at http://localhost:3000/mcp
```

Add to your editor's MCP config:

```json
{
  "mcpServers": {
    "infrawise": {
      "url": "http://localhost:3000/mcp"
    }
  }
}
```

### MCP tools

| Tool                         | What it provides                                                                                            |
| ---------------------------- | ----------------------------------------------------------------------------------------------------------- |
| `get_infra_overview`         | Complete snapshot — services, counts, high-severity findings, analysis `freshness` (age + stale flag), `configured` flag |
| `get_graph_summary`          | Full infrastructure graph — all nodes, edges, and findings                                                  |
| `get_table_schema`           | Column-level schema for named tables/collections — types, PKs, FKs, indexes, DynamoDB keys/billing mode, cost signal (no row data) |
| `analyze_function`           | Issues in a specific function — scans, missing indexes, N+1, trigger event shapes, missing IAM permissions  |
| `suggest_gsi`                | Exact GSI config for a DynamoDB table + attribute                                                           |
| `postgres_index_suggestions` | Exact `CREATE INDEX` SQL for your actual table                                                              |
| `suggest_mongo_index`        | Exact `createIndex` command for a MongoDB collection + field                                                |
| `mysql_index_suggestions`    | Exact `ALTER TABLE ADD INDEX` SQL for your MySQL table                                                      |
| `get_queue_details`          | SQS queues — DLQ status, encryption, FIFO type, visibility timeout, message counts                          |
| `get_api_routes`             | API Gateway APIs (REST, HTTP, WebSocket) — routes, HTTP methods, paths, and Lambda integrations             |
| `get_topic_details`          | SNS topics — subscription counts, protocols, and filter policies (required message attributes per subscription) |
| `get_secrets_overview`       | Secrets Manager — names, rotation status, and key names inferred from code (values never included)          |
| `get_parameter_overview`     | SSM Parameter Store — names, types, tiers (values never included)                                           |
| `get_lambda_overview`        | Lambda functions — runtime, memory, timeout, execution role ARN, triggers (SQS/SNS/DynamoDB/Kinesis/MSK/EventBridge/S3), env var key names, cost signal |
| `get_eventbridge_details`    | EventBridge rules — name, state, schedule/event pattern, target functions                                   |
| `get_s3_overview`            | S3 buckets — versioning, encryption, public access, event notifications                                     |
| `get_log_errors`             | CloudWatch error patterns and counts (no raw log messages)                                                  |
| `get_stack_outputs`          | Stack outputs and cross-stack exports parsed from local IaC files (Terraform outputs, CFN/CDK Outputs)      |
| `get_cognito_overview`       | Cognito user pools — MFA config, app client auth flows, OAuth settings, token validity (secrets never included) |
| `get_stream_details`         | Kinesis streams (shards, retention, capacity mode) and MSK clusters (state, Kafka version, brokers)          |
| `get_cache_overview`         | ElastiCache clusters — engine, encryption in transit/at rest, replication group, failover, cost signal (data never read) |

---

## CLI reference

| Command                       | What it does                                                                       |
| ----------------------------- | --------