MCP Server for AI-Assisted Development

The Moose development server includes a built-in Model Context Protocol (MCP) server that enables AI agents and IDEs to interact directly with your local development infrastructure. This allows you to use natural language to query data, inspect logs, explore infrastructure, and debug your Moose project.

What is MCP?

Model Context Protocol (MCP) is an open protocol that standardizes how AI assistants communicate with development tools and services. Moose's MCP server exposes your local development environment—including ClickHouse, Redpanda, logs, and infrastructure state—through a set of tools that AI agents can use.

Quick Start

The MCP server runs automatically when you start development mode:

moose dev

The MCP server is available at: http://localhost:4000/mcp

MooseTip:

The MCP server is enabled by default. To disable it, use moose dev --mcp=false.

Configure Your AI Client

Connect your AI assistant to the Moose MCP server. Most clients now support native HTTP transport for easier setup.

Run this command to add the Moose MCP server to your Claude Code project-level configuration:

claude mcp add --transport http moose-dev http://localhost:4000/mcp

That's it! Claude Code will automatically connect to your Moose dev server.

MooseTip:

Make sure moose dev is running before adding the server. The CLI will verify the connection.

Alternative: Manual configuration at ~/.claude/config.json

config.json

{  "mcpServers": {    "moose-dev": {      "transport": "http",      "url": "http://localhost:4000/mcp"    }  }}

Warning:

Make sure moose dev is running before using the MCP tools. The AI client will connect to http://localhost:4000/mcp.

Available Tools

The Moose MCP server provides five tools for interacting with your local development environment. Start with get_infra_map to understand your project structure, then use the other tools to query data, inspect streams, check logs, and diagnose issues.

get_infra_map (start here)

Get complete project topology showing all components with source file locations and data flow connections. This should be the first tool used.

What you get:

All tables, topics, APIs, functions, and workflows in one view
Source file paths showing where each component is defined
Data flow connections between components
Verification that your code changes are reflected in infrastructure

Optional search parameter:

Fuzzy, case-insensitive matching on component IDs and names
Example: search="user" finds UserEvents, user_table, INGRESS_User
Omit search to get the complete map

Example prompts:

"Show me the infrastructure map"

components[64]{id,type,name,source_file}:  Bar,topic,Bar,app/ingest/models.ts  local_Bar,table,Bar,app/ingest/models.ts  INGRESS_Foo,api_endpoint,POST ingest/Foo,app/ingest/models.ts  Foo__Bar,function,Foo__Bar,""  BarAggregated_MV,sql_resource,BarAggregated_MV,""  Bar_local_Bar,topic_table_sync,"Bar -> local_Bar",app/ingest/models.ts  ... connections[35]{from,to,type}:  INGRESS_Foo,Foo,produces  Foo,Foo__Bar,transforms  Foo__Bar,Bar,produces  Bar,Bar_local_Bar,ingests  Bar_local_Bar,local_Bar,ingests  ... stats:  total_components: 64  by_type: {topic: 11, table: 28, api_endpoint: 11, ...}

"Show me components related to Bar"

components[8]{id,type,name,source_file}:  Bar,topic,Bar,app/ingest/models.ts  local_Bar,table,Bar,app/ingest/models.ts  local_BarAggregated,table,BarAggregated,app/views/barAggregated.ts  EGRESS_bar,api_endpoint,GET bar,app/apis/bar.ts  Foo__Bar,function,Foo__Bar,""  BarAggregated_MV,sql_resource,BarAggregated_MV,""  Bar_local_Bar,topic_table_sync,"Bar -> local_Bar",app/ingest/models.ts  ... Search: 'Bar' | Matched: 8 component(s), 6 connection(s)

MooseTip:

Output format: Compact TOON table format maximizes readability while minimizing token usage. Component IDs can be used to construct resource URIs: moose://infra/{type}s/{id}

Use cases:

Understand project structure when starting work
Find where components are defined (source_file paths)
Trace data flow through connections
Verify code changes are reflected

get_logs

Retrieve and filter Moose development server logs for debugging and monitoring.

What you can ask for:

Filter by log level (ERROR, WARN, INFO, DEBUG, TRACE)
Limit the number of log lines returned
Search for specific text patterns in logs

Example prompts:

"Show me the last 10 ERROR logs"

Showing 10 most recent log entries from /Users/user/.moose/2025-10-10-cli.logFilters applied:  - Level: ERROR [2025-10-10T17:44:42Z ERROR] Foo -> Bar (worker 1): Unsupported SASL mechanism: undefined[2025-10-10T17:44:43Z ERROR] FooDeadLetterQueue (consumer) (worker 1): Unsupported SASL mechanism[2025-10-10T17:51:48Z ERROR] server error on API server (port 4000): connection closed...

"What WARN level logs do I have?"

Showing 6 most recent log entriesFilters applied:  - Level: WARN [2025-10-10T16:45:04Z WARN] HTTP client not configured - missing API_KEY[2025-10-10T16:50:05Z WARN] HTTP client not configured - missing API_KEY...

MooseTip:

Tip: Combine filters for better results. For example: "Show me ERROR logs with 'ClickHouse' in them" combines level filtering with search.

Use cases:

Debugging application errors
Monitoring infrastructure health
Tracking data processing issues
Finding specific events or patterns

query_olap

Execute read-only SQL queries against your local ClickHouse database.

What you can ask for:

Query table data with filters, sorting, and aggregations
Inspect table schemas and column information
Count rows and calculate statistics
List all tables in your database
Results in table or JSON format

Example prompts:

"What columns are in the UserEvents_1_0 table?"

Query executed successfully. Rows returned: 4 name        type               default_type  default_expression  commentuserId      StringeventType   Stringtimestamp   Float64metadata    Nullable(String)

"List all tables and their engines"

Query executed successfully. Rows returned: 29 name                         engineBar                          MergeTreeBasicTypes                   MergeTreeUserEvents_1_0               MergeTreeUserEvents_2_0               ReplacingMergeTreeReplicatedMergeTreeTest      ReplicatedMergeTreeBarAggregated_MV             MaterializedView...

"Count the number of rows in Bar"

Query executed successfully. Rows returned: 1 total_rows0

MooseTip:

Tip: Ask the AI to discover table names first using "What tables exist in my project?" before querying them. Table names are case-sensitive in ClickHouse.

Use cases:

Exploring data during development
Validating data transformations
Checking table schemas
Debugging SQL queries
Analyzing data patterns

Safety: Only read-only operations are permitted (SELECT, SHOW, DESCRIBE, EXPLAIN). Write operations (INSERT, UPDATE, DELETE) and DDL statements (CREATE, ALTER, DROP) are blocked.

get_stream_sample

Sample recent messages from Kafka/Redpanda streaming topics.

What you can ask for:

View recent messages from any stream/topic
Specify how many messages to sample
Get results in JSON or pretty-printed format
Inspect message structure and content

Example prompts:

"Sample 5 messages from the Bar topic"

{  "stream_name": "Bar",  "message_count": 5,  "partition_count": 1,  "messages": [    {      "primaryKey": "e90c93be-d28b-47d6-b783-5725655c044f",      "utcTimestamp": "+057480-11-24T20:39:59.000Z",      "hasText": true,      "textLength": 107    },    {      "primaryKey": "b974f830-f28a-4a95-b61c-f65bfc607795",      "utcTimestamp": "+057370-11-04T17:11:51.000Z",      "hasText": true,      "textLength": 166    },    ...  ]}

"What data is flowing through the BasicTypes stream?" (pretty format)

# Stream Sample: BasicTypes Retrieved 3 message(s) from 1 partition(s) Message 1{  "id": "bt-001",  "timestamp": "2024-10-09T12:00:00Z",  "stringField": "hello world",  "numberField": 42,  "booleanField": true} Message 2{  "id": "bt-002",  "timestamp": "2024-10-09T12:05:00Z",  "stringField": "test",  "numberField": 100,  "booleanField": false}...

MooseTip:

Tip: Use "List all streaming topics" first to discover available streams in your project.

Use cases:

Debugging data flow issues
Validating streaming transformations
Inspecting message formats
Troubleshooting pipeline failures

get_issues

Proactively scan your Moose project for health issues and operational problems.

What it checks:

Stuck mutations (long-running operations)
Replication errors on replicated tables
S3Queue ingestion failures
Data parts and merge issues
Background operation problems

Example prompts:

"Check for any infrastructure issues"

infrastructure_type: clickhouseissues[2]{severity,component_type,component_name,issue_type,message}:  error,table,UserEvents,stuck_mutation,"Mutation 0000000003 stuck for 2h 15m"  warning,table,ReplicatedTable,replication_lag,"Replication lag: 15s behind leader" summary:  total_issues: 2  by_severity: {error: 1, warning: 1}

"Any problems with my tables?"

infrastructure_type: clickhouseissues[0]:summary:  total_issues: 0  by_severity: {}  by_component: {}

MooseTip:

Tip: Run this when troubleshooting errors or performance issues. It automatically checks relevant diagnostics based on your infrastructure configuration.

Use cases:

Troubleshooting unexplained errors
Performance degradation investigation
Proactive health monitoring
Pre-deployment validation

Example Workflows

Understanding a New Project

You: "Can you explain this Moose project structure?" AI: [Uses get_infra_map to see topology] AI: This project has 11 topics, 28 tables, 11 API endpoints, and 3 functions.Data flows like this:- INGRESS APIs send data to topics (e.g., INGRESS_Foo → Foo topic)- Functions transform between topics (Foo → Foo__Bar → Bar)- Topics sync to tables (Bar_local_Bar sync → local_Bar table)- SQL resources create views (BarAggregated_MV) All components are defined in app/ingest/models.ts and app/functions/.

Debugging a Data Pipeline

You: "My users table is empty, can you help me debug?" AI: Let me check the infrastructure and data flow.[Uses get_infra_map search="user" to see the pipeline][Uses get_issues to check for infrastructure problems][Uses get_logs level="ERROR" to check for errors][Uses query_olap to verify table is empty][Uses get_stream_sample to check if data is in the source stream] AI: I found the issue - there are errors in the logs showing the streamingfunction is failing. The stream has data, but it's not reaching the table.

Validating Data Transformations

You: "Did my streaming function correctly transform the user data?" AI: [Uses get_stream_sample on source stream][Uses query_olap on destination table] AI: Yes, the transformation is working. I compared the source streammessages with the destination table records, and the enrichmentfields are being added correctly.

Advanced Configuration

Custom Port

If you're running Moose on a non-default port, update the MCP configuration:

export MOOSE_HTTP_PORT=8080
moose dev

Then update your MCP client configuration to use port 8080 instead of 4000.

Disabling the MCP Server

To run development mode without the MCP server:

moose dev --mcp=false

Production Considerations

Warning:

The MCP server is designed for local development only. It provides direct access to your infrastructure and should never be exposed in production environments.

The MCP server:

Runs only in development mode (moose dev)
Does not run in production mode (moose prod)
Provides read-only access to sensitive infrastructure
Should not be exposed over networks or proxied externally

LLM-Optimized Documentation Feeds

Documentation Index

https://docs.fiveonefour.com/llm.md returns a table of contents linking to all public pages. LLMs can discover what exists, then fetch only what they need — avoiding context window limits.

Per-Page Markdown Endpoints

Append .md to any doc URL to get clean markdown:

https://docs.fiveonefour.com/moosestack/olap/model-table.md — Defaults to TypeScript
https://docs.fiveonefour.com/moosestack/olap/model-table.md?lang=python — Specific language

Useful for AI agents fetching specific pages on-demand.

Context7

Context7 indexes MooseStack documentation and code examples, letting LLMs pull context on-demand.

Setup: Install Context7 for your IDE

Usage: Add use context7 to prompts about MooseStack (or create a rule in your workspace to automatically use context7 for anything MooseStack):

Show me how to create a streaming function in MooseStack using TypeScript. use context7

Best together: Context7 provides MooseStack framework documentation, while the Moose MCP server provides access to your local project's data, logs, and infrastructure.

Every doc page has a Copy Page dropdown with two options:

Copy as Markdown — copies clean markdown to your clipboard
Open Markdown — opens the .md endpoint in a new tab

Troubleshooting

MCP Tools Not Appearing

Verify moose dev is running: curl http://localhost:4000/mcp
Check your AI client's MCP configuration is correct
Restart your AI client after updating configuration
Check the Moose logs for MCP-related errors: moose logs --filter mcp

Connection Errors

If your AI client can't connect to the MCP server:

# Check if the dev server is running
curl http://localhost:4000/health
 
# Check MCP endpoint specifically
curl -X POST http://localhost:4000/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize"}'

Empty Results

If tools return no data:

Verify your dev server has been running long enough to generate data
Check that infrastructure has been created: moose ls
Try ingesting test data: moose peek <table_name>

Local Development - Development mode overview
Moose CLI Reference - CLI commands and flags
Model Context Protocol - MCP specification

MCP Server for AI-Assisted Development

What is MCP?

Quick Start

The MCP server runs automatically when you start development mode:

moose dev

The MCP server is available at: http://localhost:4000/mcp

MooseTip:

The MCP server is enabled by default. To disable it, use moose dev --mcp=false.

Configure Your AI Client

Connect your AI assistant to the Moose MCP server. Most clients now support native HTTP transport for easier setup.

Run this command to add the Moose MCP server to your Claude Code project-level configuration:

claude mcp add --transport http moose-dev http://localhost:4000/mcp

That's it! Claude Code will automatically connect to your Moose dev server.

MooseTip:

Make sure moose dev is running before adding the server. The CLI will verify the connection.

Alternative: Manual configuration at ~/.claude/config.json

config.json

{  "mcpServers": {    "moose-dev": {      "transport": "http",      "url": "http://localhost:4000/mcp"    }  }}

Warning:

Make sure moose dev is running before using the MCP tools. The AI client will connect to http://localhost:4000/mcp.

Available Tools

get_infra_map (start here)

Get complete project topology showing all components with source file locations and data flow connections. This should be the first tool used.

What you get:

All tables, topics, APIs, functions, and workflows in one view
Source file paths showing where each component is defined
Data flow connections between components
Verification that your code changes are reflected in infrastructure

Optional search parameter:

Fuzzy, case-insensitive matching on component IDs and names
Example: search="user" finds UserEvents, user_table, INGRESS_User
Omit search to get the complete map

Example prompts:

"Show me the infrastructure map"

components[64]{id,type,name,source_file}:  Bar,topic,Bar,app/ingest/models.ts  local_Bar,table,Bar,app/ingest/models.ts  INGRESS_Foo,api_endpoint,POST ingest/Foo,app/ingest/models.ts  Foo__Bar,function,Foo__Bar,""  BarAggregated_MV,sql_resource,BarAggregated_MV,""  Bar_local_Bar,topic_table_sync,"Bar -> local_Bar",app/ingest/models.ts  ... connections[35]{from,to,type}:  INGRESS_Foo,Foo,produces  Foo,Foo__Bar,transforms  Foo__Bar,Bar,produces  Bar,Bar_local_Bar,ingests  Bar_local_Bar,local_Bar,ingests  ... stats:  total_components: 64  by_type: {topic: 11, table: 28, api_endpoint: 11, ...}

"Show me components related to Bar"

components[8]{id,type,name,source_file}:  Bar,topic,Bar,app/ingest/models.ts  local_Bar,table,Bar,app/ingest/models.ts  local_BarAggregated,table,BarAggregated,app/views/barAggregated.ts  EGRESS_bar,api_endpoint,GET bar,app/apis/bar.ts  Foo__Bar,function,Foo__Bar,""  BarAggregated_MV,sql_resource,BarAggregated_MV,""  Bar_local_Bar,topic_table_sync,"Bar -> local_Bar",app/ingest/models.ts  ... Search: 'Bar' | Matched: 8 component(s), 6 connection(s)

MooseTip:

Output format: Compact TOON table format maximizes readability while minimizing token usage. Component IDs can be used to construct resource URIs: moose://infra/{type}s/{id}

Use cases:

Understand project structure when starting work
Find where components are defined (source_file paths)
Trace data flow through connections
Verify code changes are reflected

get_logs

Retrieve and filter Moose development server logs for debugging and monitoring.

What you can ask for:

Filter by log level (ERROR, WARN, INFO, DEBUG, TRACE)
Limit the number of log lines returned
Search for specific text patterns in logs

Example prompts:

"Show me the last 10 ERROR logs"

Showing 10 most recent log entries from /Users/user/.moose/2025-10-10-cli.logFilters applied:  - Level: ERROR [2025-10-10T17:44:42Z ERROR] Foo -> Bar (worker 1): Unsupported SASL mechanism: undefined[2025-10-10T17:44:43Z ERROR] FooDeadLetterQueue (consumer) (worker 1): Unsupported SASL mechanism[2025-10-10T17:51:48Z ERROR] server error on API server (port 4000): connection closed...

"What WARN level logs do I have?"

Showing 6 most recent log entriesFilters applied:  - Level: WARN [2025-10-10T16:45:04Z WARN] HTTP client not configured - missing API_KEY[2025-10-10T16:50:05Z WARN] HTTP client not configured - missing API_KEY...

MooseTip:

Tip: Combine filters for better results. For example: "Show me ERROR logs with 'ClickHouse' in them" combines level filtering with search.

Use cases:

Debugging application errors
Monitoring infrastructure health
Tracking data processing issues
Finding specific events or patterns

query_olap

Execute read-only SQL queries against your local ClickHouse database.

What you can ask for:

Query table data with filters, sorting, and aggregations
Inspect table schemas and column information
Count rows and calculate statistics
List all tables in your database
Results in table or JSON format

Example prompts:

"What columns are in the UserEvents_1_0 table?"

Query executed successfully. Rows returned: 4 name        type               default_type  default_expression  commentuserId      StringeventType   Stringtimestamp   Float64metadata    Nullable(String)

"List all tables and their engines"

Query executed successfully. Rows returned: 29 name                         engineBar                          MergeTreeBasicTypes                   MergeTreeUserEvents_1_0               MergeTreeUserEvents_2_0               ReplacingMergeTreeReplicatedMergeTreeTest      ReplicatedMergeTreeBarAggregated_MV             MaterializedView...

"Count the number of rows in Bar"

Query executed successfully. Rows returned: 1 total_rows0

MooseTip:

Tip: Ask the AI to discover table names first using "What tables exist in my project?" before querying them. Table names are case-sensitive in ClickHouse.

Use cases:

Exploring data during development
Validating data transformations
Checking table schemas
Debugging SQL queries
Analyzing data patterns

Safety: Only read-only operations are permitted (SELECT, SHOW, DESCRIBE, EXPLAIN). Write operations (INSERT, UPDATE, DELETE) and DDL statements (CREATE, ALTER, DROP) are blocked.

get_stream_sample

Sample recent messages from Kafka/Redpanda streaming topics.

What you can ask for:

View recent messages from any stream/topic
Specify how many messages to sample
Get results in JSON or pretty-printed format
Inspect message structure and content

Example prompts:

"Sample 5 messages from the Bar topic"

{  "stream_name": "Bar",  "message_count": 5,  "partition_count": 1,  "messages": [    {      "primaryKey": "e90c93be-d28b-47d6-b783-5725655c044f",      "utcTimestamp": "+057480-11-24T20:39:59.000Z",      "hasText": true,      "textLength": 107    },    {      "primaryKey": "b974f830-f28a-4a95-b61c-f65bfc607795",      "utcTimestamp": "+057370-11-04T17:11:51.000Z",      "hasText": true,      "textLength": 166    },    ...  ]}

"What data is flowing through the BasicTypes stream?" (pretty format)

# Stream Sample: BasicTypes Retrieved 3 message(s) from 1 partition(s) Message 1{  "id": "bt-001",  "timestamp": "2024-10-09T12:00:00Z",  "stringField": "hello world",  "numberField": 42,  "booleanField": true} Message 2{  "id": "bt-002",  "timestamp": "2024-10-09T12:05:00Z",  "stringField": "test",  "numberField": 100,  "booleanField": false}...

MooseTip:

Tip: Use "List all streaming topics" first to discover available streams in your project.

Use cases:

Debugging data flow issues
Validating streaming transformations
Inspecting message formats
Troubleshooting pipeline failures

get_issues

Proactively scan your Moose project for health issues and operational problems.

What it checks:

Stuck mutations (long-running operations)
Replication errors on replicated tables
S3Queue ingestion failures
Data parts and merge issues
Background operation problems

Example prompts:

"Check for any infrastructure issues"

infrastructure_type: clickhouseissues[2]{severity,component_type,component_name,issue_type,message}:  error,table,UserEvents,stuck_mutation,"Mutation 0000000003 stuck for 2h 15m"  warning,table,ReplicatedTable,replication_lag,"Replication lag: 15s behind leader" summary:  total_issues: 2  by_severity: {error: 1, warning: 1}

"Any problems with my tables?"

infrastructure_type: clickhouseissues[0]:summary:  total_issues: 0  by_severity: {}  by_component: {}

MooseTip:

Tip: Run this when troubleshooting errors or performance issues. It automatically checks relevant diagnostics based on your infrastructure configuration.

Use cases:

Troubleshooting unexplained errors
Performance degradation investigation
Proactive health monitoring
Pre-deployment validation

Example Workflows

Understanding a New Project

You: "Can you explain this Moose project structure?" AI: [Uses get_infra_map to see topology] AI: This project has 11 topics, 28 tables, 11 API endpoints, and 3 functions.Data flows like this:- INGRESS APIs send data to topics (e.g., INGRESS_Foo → Foo topic)- Functions transform between topics (Foo → Foo__Bar → Bar)- Topics sync to tables (Bar_local_Bar sync → local_Bar table)- SQL resources create views (BarAggregated_MV) All components are defined in app/ingest/models.ts and app/functions/.

Debugging a Data Pipeline

You: "My users table is empty, can you help me debug?" AI: Let me check the infrastructure and data flow.[Uses get_infra_map search="user" to see the pipeline][Uses get_issues to check for infrastructure problems][Uses get_logs level="ERROR" to check for errors][Uses query_olap to verify table is empty][Uses get_stream_sample to check if data is in the source stream] AI: I found the issue - there are errors in the logs showing the streamingfunction is failing. The stream has data, but it's not reaching the table.

Validating Data Transformations

You: "Did my streaming function correctly transform the user data?" AI: [Uses get_stream_sample on source stream][Uses query_olap on destination table] AI: Yes, the transformation is working. I compared the source streammessages with the destination table records, and the enrichmentfields are being added correctly.

Advanced Configuration

Custom Port

If you're running Moose on a non-default port, update the MCP configuration:

export MOOSE_HTTP_PORT=8080
moose dev

Then update your MCP client configuration to use port 8080 instead of 4000.

Disabling the MCP Server

To run development mode without the MCP server:

moose dev --mcp=false

Production Considerations

Warning:

The MCP server is designed for local development only. It provides direct access to your infrastructure and should never be exposed in production environments.

The MCP server:

Runs only in development mode (moose dev)
Does not run in production mode (moose prod)
Provides read-only access to sensitive infrastructure
Should not be exposed over networks or proxied externally

LLM-Optimized Documentation Feeds

Documentation Index

https://docs.fiveonefour.com/llm.md returns a table of contents linking to all public pages. LLMs can discover what exists, then fetch only what they need — avoiding context window limits.

Per-Page Markdown Endpoints

Append .md to any doc URL to get clean markdown:

https://docs.fiveonefour.com/moosestack/olap/model-table.md — Defaults to TypeScript
https://docs.fiveonefour.com/moosestack/olap/model-table.md?lang=python — Specific language

Useful for AI agents fetching specific pages on-demand.

Context7

Context7 indexes MooseStack documentation and code examples, letting LLMs pull context on-demand.

Setup: Install Context7 for your IDE

Usage: Add use context7 to prompts about MooseStack (or create a rule in your workspace to automatically use context7 for anything MooseStack):

Show me how to create a streaming function in MooseStack using TypeScript. use context7

Best together: Context7 provides MooseStack framework documentation, while the Moose MCP server provides access to your local project's data, logs, and infrastructure.

Every doc page has a Copy Page dropdown with two options:

Copy as Markdown — copies clean markdown to your clipboard
Open Markdown — opens the .md endpoint in a new tab

Troubleshooting

MCP Tools Not Appearing

Verify moose dev is running: curl http://localhost:4000/mcp
Check your AI client's MCP configuration is correct
Restart your AI client after updating configuration
Check the Moose logs for MCP-related errors: moose logs --filter mcp

Connection Errors

If your AI client can't connect to the MCP server:

# Check if the dev server is running
curl http://localhost:4000/health
 
# Check MCP endpoint specifically
curl -X POST http://localhost:4000/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize"}'

Empty Results

If tools return no data:

Verify your dev server has been running long enough to generate data
Check that infrastructure has been created: moose ls
Try ingesting test data: moose peek <table_name>

Local Development - Development mode overview
Moose CLI Reference - CLI commands and flags
Model Context Protocol - MCP specification