infrawise

Infrawise

Understand your infrastructure, not just your code.

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.

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

npm install -g infrawise

or use without installing:

npx infrawise init

Quick start

1. Initialize in your repo

cd your-project
infrawise init

Detects your AWS profile and region, asks a few questions, writes infrawise.yaml. That's the only file it creates in your project.

2. Validate everything is connected

infrawise doctor

3. Run analysis

infrawise analyze

Or skip this step — infrawise dev auto-runs analysis if no cache exists.

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

Step 1: Start the MCP server

infrawise dev

  ✔ Config loaded          infrawise.yaml
  ✔ Cached analysis loaded 42 nodes · 18 edges · 7 finding(s)
  ✔ Server running

  ┌────────────────────────────────────────────────────┐
  │ MCP Server                                        │
  ├────────────────────────────────────────────────────┤
  │ POST http://localhost:3000/mcp                    │
  │ GET  http://localhost:3000/health                 │
  ├────────────────────────────────────────────────────┤
  │ Tools (13 active)                                 │
  │ get_infra_overview · get_graph_summary            │
  │ ...                                               │
  └────────────────────────────────────────────────────┘

  Watching for file changes... Press Ctrl+C to stop

Step 2: Add to your editor settings

Claude Code — edit .claude/settings.json in your repo (project-level) or ~/.claude/settings.json (global):

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

To let Claude Code manage the server lifecycle automatically:

{
  "mcpServers": {
    "infrawise": {
      "command": "infrawise",
      "args": ["dev"]
    }
  }
}

Cursor and Windsurf — add http://localhost:3000/mcp as an MCP server in editor settings.

MCP tools

Tool	What it provides
get_infra_overview	Complete snapshot — all services, counts, and high-severity findings
get_graph_summary	Full infrastructure graph — all nodes, edges, and findings
analyze_function	Issues in a specific function — scans, missing indexes, N+1, trigger event shapes
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, message counts
get_topic_details	SNS topics — subscription counts and protocols
get_secrets_overview	Secrets Manager — names and rotation status (values never included)
get_parameter_overview	SSM Parameter Store — names, types, tiers (values never included)
get_lambda_overview	Lambda functions — runtime, memory, timeout, triggers (SQS/DynamoDB/Kinesis/EventBridge), env var key names
get_eventbridge_details	EventBridge rules — name, state, schedule/event pattern, target functions
get_log_errors	CloudWatch error patterns and counts (no raw log messages)

CLI reference

Command	What it does
infrawise init	Detect AWS + repo, generate infrawise.yaml
infrawise auth	Select or switch AWS profile
infrawise analyze	Scan repo + AWS, build graph, print findings
infrawise dev	Start MCP server — auto-analyzes if no cache, watches files for live refresh
infrawise doctor	Validate AWS access, DB connectivity, and config

Configuration

infrawise.yaml is generated by infrawise init and lives in your repo root. Every service must be explicitly enabled: true — infrawise never connects to anything not listed in config.

Connection strings support ${ENV_VAR} substitution so passwords never need to be committed:

postgres:
  enabled: true
  connectionString: postgresql://infrawise_ro:${DB_PASSWORD}@host:5432/mydb

Full example:

project: payments-service

aws:
  profile: default          # AWS profile from ~/.aws/credentials
  region: ap-south-1

dynamodb:
  enabled: true
  includeTables:            # omit to include all tables
    - Orders
    - Users

postgres:
  enabled: true
  connectionString: postgresql://infrawise_ro:${DB_PASSWORD}@host:5432/mydb

mysql:
  enabled: false
  connectionString: ""

mongodb:
  enabled: false
  connectionString: ""

sqs:
  enabled: true

sns:
  enabled: true

ssm:
  enabled: true
  paths: []                 # filter by prefix e.g. ["/myapp/prod"]

secretsManager:
  enabled: true

lambda:
  enabled: true
  includeFunctions:           # omit to include all functions
    - processOrders
    - generateReport

eventbridge:
  enabled: true

rds:
  enabled: false

kafka:
  enabled: false

cloudwatchLogs:
  enabled: false
  logGroupPrefixes: []
  windowHours: 24

analysis:
  sampleSize: 100

AWS setup

Infrawise is read-only. Minimum IAM policy required:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "dynamodb:ListTables",
        "dynamodb:DescribeTable"
      ],
      "Resource": "*"
    }
  ]
}

For SSO profiles, log in before running infrawise:

aws sso login --profile myprofile

PostgreSQL setup (optional)

Create a read-only user for infrawise:

CREATE USER infrawise_ro WITH PASSWORD 'yourpassword';
GRANT CONNECT ON DATABASE yourdb TO infrawise_ro;
GRANT USAGE ON SCHEMA public TO infrawise_ro;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO infrawise_ro;

For Amazon RDS: allow inbound on port 5432 from your machine's IP in the security group.

Analysis capabilities

Infrawise has two analysis layers:

Infrastructure analysis (all languages)

Works from AWS APIs, database schema introspection, and IaC files — no dependency on application code:

Service	What it checks
DynamoDB schema	Tables, GSIs, partition keys
PostgreSQL / MySQL schema	Tables, indexes, column types
MongoDB schema	Collections, indexes
SQS	Missing DLQs, unencrypted queues, large backlogs
Kafka (kafkajs)	Producer/consumer topic mapping from code
Secrets Manager	Missing secret rotation
Lambda	Default memory (128 MB), high timeouts, triggers (SQS/DynamoDB/Kinesis/EventBridge), missing DLQ on trigger source
EventBridge	Rules, schedules, event patterns, target Lambda functions
RDS	Publicly accessible, no backups, unencrypted, no deletion protection, single-AZ
CloudWatch Logs	Log groups with no retention policy
Terraform / CloudFormation / CDK	IaC drift vs deployed state

Code correlation analysis (TypeScript / JavaScript)

Uses ts-morph AST analysis to detect which functions call which tables and how:

Analyzer	Severity	What it detects
Full Table Scan (DynamoDB)	High	.scan() calls without filters
Missing GSI	Medium	Queries on attributes without a matching GSI
Hot Partition	Medium	5+ distinct code paths hitting the same table
Missing Index (PostgreSQL)	Medium	Tables queried without indexes
N+1 Query	Medium	Repeated query patterns from ORM loops
Large SELECT	Low	SELECT * usage
Missing MySQL Index	Medium	MySQL tables queried without indexes
MySQL Full Table Scan	High	Full table scan patterns in MySQL queries
Missing Mongo Index	Medium	Collections queried without secondary indexes
Collection Scan	High	find() calls without filter predicates

Non-TypeScript/JavaScript projects still get full value from infrastructure-level analyzers — code correlation (function-to-table mapping, N+1 patterns) is skipped.

The scanner supports: AWS SDK v3/v2 for DynamoDB, pg/Prisma/Knex for PostgreSQL, mysql2/Knex for MySQL, driver/Mongoose for MongoDB, AWS SDK v3 for SQS/SNS/SSM/Secrets/Lambda, and kafkajs for Kafka topics (producer/consumer).

How it works

• Infrawise scans your repository and infrastructure metadata
• A graph engine maps services, schemas, indexes, and query patterns
• Rule-based analyzers detect infrastructure and query anti-patterns
• The resulting context is exposed through MCP
• AI coding assistants query this context while generating code

Deterministic analysis

Infrawise does not use an LLM to analyze your infrastructure. All extraction and analysis are deterministic: AST parsing, schema introspection, rule-based analyzers, and graph correlation. LLMs are only consumers of the generated context through MCP.

Security

• Read-only — never writes to AWS or your database, never executes DDL
• Local-first — everything runs on your machine, nothing sent to external servers
• No telemetry — zero data collection
• Credentials — uses your existing AWS credential chain, never stored by infrawise

Architecture overview

Your repo (any language)          Your repo (TS/JS only)
        │                                  │
        │                    Repository Scanner (ts-morph AST)
        │                     which functions → which tables
        │                                  │
┌───────┴──────────────────────────────────┴────────────┐
│  infrawise analyze                                    │
│                                                       │
│  AWS APIs / DB schema / IaC files  +  Code ops (opt)  │
│         (works for any project)      (TS/JS only)     │
│                          │                            │
│                     Graph Engine                      │
│                   (nodes + edges)                     │
│                          │                            │
│                   Analyzer Engine                     │
│               (rule-based, deterministic)             │
└─────────────────────────┬─────────────────────────────┘
                          │
               ┌──────────────────┐
               │   MCP Server     │ ◄── Claude Code
               │  localhost:3000  │ ◄── Cursor
               └──────────────────┘ ◄── Windsurf

Source layout

src/
  types.ts      Shared type definitions
  core/         Config (Zod + YAML), logger (Pino), local cache
  graph/        Graph engine — nodes, edges, builder
  adapters/     Flat extractors: dynamodb.ts, postgres.ts, mysql.ts,
                mongodb.ts, aws.ts, logs.ts, terraform.ts
  analyzers/    23 rule-based analyzers
  context/      Repository scanner (ts-morph AST)
  server/       Fastify MCP server (@modelcontextprotocol/sdk, Streamable HTTP)
  cli/          CLI commands (Commander.js)

Current limitations

• Code-level correlation supports TypeScript and JavaScript only
• Dynamically constructed queries may not always be resolved statically
• Runtime tracing is not yet implemented
• Large monorepos may require future incremental analysis optimization

Roadmap

Feature roadmap is tracked in the GitHub Project. Priorities, complexity, and virality scores are visible there. Feature requests and upvotes welcome.

Planned

• Runtime tracing integration
• Incremental analysis for large monorepos
• Kubernetes workload graphing
• VS Code extension
• Infrastructure drift detection
• MSK (Amazon Managed Streaming for Apache Kafka) — cluster metadata + topic listing via MSK API and Kafka admin client

Under consideration

• OpenTelemetry integration
• CI/CD reporting mode
• Multi-repository graph correlation

Demo

The demo/localstack/ directory runs infrawise against real AWS APIs emulated locally via LocalStack — an open-source tool that spins up a full AWS environment in Docker so you can test AWS integrations at zero cost, with no real AWS account needed. See demo/localstack/README.md for setup instructions.

Contributing

Prerequisites

Node.js 24+, pnpm 9+, AWS CLI (for integration testing).

Setup

git clone https://github.com/Sidd27/infrawise
cd infrawise
pnpm install
pnpm build

Development workflow

pnpm build        # compile
pnpm test         # run all tests
pnpm typecheck    # TypeScript strict check
pnpm lint         # ESLint

Adding a new analyzer

• Create your analyzer in src/analyzers/
• Implement the Analyzer interface:

export class MyAnalyzer implements Analyzer {
  name = 'MyAnalyzer';
  async analyze(graph: SystemGraph): Promise<Finding[]> { ... }
}

• Export it from src/analyzers/index.ts
• Add tests in src/analyzers/__tests__/

Adding a new database adapter

• Create your extractor as src/adapters/yourdb.ts
• Export a function returning Promise<YourTableMetadata[]>
• Add the metadata type to src/types.ts if needed
• Wire it into src/cli/commands/analyze.ts

Releasing

pnpm release patch    # 0.1.2 → 0.1.3  (bug fixes)
pnpm release minor    # 0.1.2 → 0.2.0  (new features, backwards compatible)
pnpm release major    # 0.1.2 → 1.0.0  (breaking changes)
pnpm release 1.5.0    # explicit version

Bumps package.json, commits, tags, pushes, and creates a draft GitHub release with notes from commit messages. Then publish the draft on GitHub to trigger npm publish.

PR checklist

• pnpm lint passes
• pnpm typecheck passes
• pnpm test passes
• New analyzers have unit tests with mock graph data
• No hardcoded AWS regions or credentials

License

MIT