Techwavedev Agi Agent Kit Render Automation

Render Automation via Rube MCP

Automate Render cloud platform operations through Composio's Render toolkit via Rube MCP.

Prerequisites

• Rube MCP must be connected (RUBE_SEARCH_TOOLS available)
• Active Render connection via RUBE_MANAGE_CONNECTIONS with toolkit render
• Always call RUBE_SEARCH_TOOLS first to get current tool schemas

Setup

Get Rube MCP: Add https://rube.app/mcp as an MCP server in your client configuration. No API keys needed — just add the endpoint and it works.

1. Verify Rube MCP is available by confirming RUBE_SEARCH_TOOLS responds
2. Call RUBE_MANAGE_CONNECTIONS with toolkit render
3. If connection is not ACTIVE, follow the returned auth link to complete Render authentication
4. Confirm connection status shows ACTIVE before running any workflows

Core Workflows

1. List and Browse Services

When to use: User wants to find or inspect Render services (web services, static sites, workers, cron jobs)

Tool sequence:

1. RENDER_LIST_SERVICES - List all services with optional filters [Required]

Key parameters:

• name: Filter services by name substring
• type: Filter by service type ('web_service', 'static_site', 'private_service', 'background_worker', 'cron_job')
• limit: Maximum results per page (default 20, max 100)
• cursor: Pagination cursor from previous response

Pitfalls:

• Service types must match exact enum values: 'web_service', 'static_site', 'private_service', 'background_worker', 'cron_job'
• Pagination uses cursor-based approach; follow cursor until absent
• Name filter is substring-based, not exact match
• Service IDs follow the format 'srv-xxxxxxxxxxxx'
• Default limit is 20; set higher for comprehensive listing

2. Trigger Deployments

When to use: User wants to manually deploy or redeploy a service

Tool sequence:

1. RENDER_LIST_SERVICES - Find the service to deploy [Prerequisite]
2. RENDER_TRIGGER_DEPLOY - Trigger a new deployment [Required]
3. RENDER_RETRIEVE_DEPLOY - Monitor deployment progress [Optional]

Key parameters:

• For TRIGGER_DEPLOY:
  • serviceId: Service ID to deploy (required, format: 'srv-xxxxxxxxxxxx')
  • clearCache: Set true to clear build cache before deploying
• For RETRIEVE_DEPLOY:
  • serviceId: Service ID
  • deployId: Deploy ID from trigger response (format: 'dep-xxxxxxxxxxxx')

Pitfalls:

• serviceId is required; resolve via LIST_SERVICES first
• Service IDs start with 'srv-' prefix
• Deploy IDs start with 'dep-' prefix
• clearCache: true forces a clean build; takes longer but resolves cache-related issues
• Deployment is asynchronous; use RETRIEVE_DEPLOY to poll status
• Triggering a deploy while another is in progress may queue the new one

3. Monitor Deployment Status

When to use: User wants to check the progress or result of a deployment

Tool sequence:

1. RENDER_RETRIEVE_DEPLOY - Get deployment details and status [Required]

Key parameters:

• serviceId: Service ID (required)
• deployId: Deployment ID (required)
• Response includes status, createdAt, updatedAt, finishedAt, commit

Pitfalls:

• Both serviceId and deployId are required
• Deploy statuses include: 'created', 'build_in_progress', 'update_in_progress', 'live', 'deactivated', 'build_failed', 'update_failed', 'canceled'
• 'live' indicates successful deployment
• 'build_failed' or 'update_failed' indicate deployment errors
• Poll at reasonable intervals (10-30 seconds) to avoid rate limits

4. Manage Projects

When to use: User wants to list and organize Render projects

Tool sequence:

1. RENDER_LIST_PROJECTS - List all projects [Required]

Key parameters:

• limit: Maximum results per page (max 100)
• cursor: Pagination cursor from previous response

Pitfalls:

• Projects group related services together
• Pagination uses cursor-based approach
• Project IDs are used for organizational purposes
• Not all services may be assigned to a project

Common Patterns

ID Resolution

Service name -> Service ID:

1. Call RENDER_LIST_SERVICES with name=service_name
2. Find service by name in results
3. Extract id (format: 'srv-xxxxxxxxxxxx')

Deployment lookup:

1. Store deployId from RENDER_TRIGGER_DEPLOY response
2. Call RENDER_RETRIEVE_DEPLOY with serviceId and deployId
3. Check status for completion

Deploy and Monitor Pattern

1. RENDER_LIST_SERVICES -> find service by name -> get serviceId
2. RENDER_TRIGGER_DEPLOY with serviceId -> get deployId
3. Loop: RENDER_RETRIEVE_DEPLOY with serviceId + deployId
4. Check status: 'live' = success, 'build_failed'/'update_failed' = error
5. Continue polling until terminal state reached

Pagination

• Use cursor from response for next page
• Continue until cursor is absent or results are empty
• Both LIST_SERVICES and LIST_PROJECTS use cursor-based pagination
• Set limit to max (100) for fewer pagination rounds

Known Pitfalls

Service IDs:

• Always prefixed with 'srv-' (e.g., 'srv-abcd1234efgh')
• Deploy IDs prefixed with 'dep-' (e.g., 'dep-d2mqkf9r0fns73bham1g')
• Always resolve service names to IDs via LIST_SERVICES

Service Types:

• Must use exact enum values when filtering
• Available types: web_service, static_site, private_service, background_worker, cron_job
• Different service types have different deployment behaviors

Deployment Behavior:

• Deployments are asynchronous; always poll for completion
• Clear cache deploys take longer but resolve stale cache issues
• Failed deploys do not roll back automatically; the previous version stays live
• Concurrent deploy triggers may be queued

Rate Limits:

• Render API has rate limits
• Avoid rapid polling; use 10-30 second intervals
• Bulk operations should be throttled

Response Parsing:

• Response data may be nested under data key
• Timestamps use ISO 8601 format
• Parse defensively with fallbacks for optional fields

Quick Reference

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Cache workflow configurations and automation patterns. Retrieve prior pipeline designs to avoid re-building similar flows from scratch.

# Check for prior workflow/automation context before starting
python3 execution/memory_manager.py auto --query "automation patterns and workflow configurations for Render Automation"

Storing Results

After completing work, store workflow/automation decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Workflow: automated data pipeline with retry logic, dead-letter queue, and Slack alerts on failure" \
  --type technical --project <project> \
  --tags render-automation workflow

Multi-Agent Collaboration

Share workflow state with other agents so they can trigger, monitor, or extend the automation.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Workflow automation deployed — pipeline processing 1000+ events/day with 99.9% success rate" \
  --project <project>

Playbook Engine

Combine this skill with others using the Playbook Engine (execution/workflow_engine.py) for guided multi-step automation with progress tracking.

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