{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-render","slug":"render","name":"Render","type":"mcp","url":"https://render.com/docs/mcp-server","page_url":"https://unfragile.ai/render","categories":["mcp-servers"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-render__cap_0","uri":"capability://automation.workflow.service.lifecycle.management.via.natural.language","name":"service-lifecycle-management-via-natural-language","description":"Enables AI agents to create and configure new Render services through natural language prompts that are translated into Render API calls. The MCP server acts as a bridge between conversational AI interfaces (Claude, Cursor, etc.) and Render's infrastructure provisioning APIs, allowing agents to interpret user intent like 'spin up a Node.js web service' and execute the corresponding service creation workflow with environment variable configuration.","intents":["I want my AI agent to automatically provision new services based on deployment requests","I need to let Claude create staging environments on demand without manual API calls","I want to enable rapid service iteration by having agents spin up test services"],"best_for":["DevOps teams integrating Render infrastructure into AI-driven deployment workflows","Solo developers using Claude/Cursor as an IDE for full-stack development with auto-provisioning","Teams building internal tools that need to dynamically create Render services"],"limitations":["Only supports environment variable modification as a destructive operation; no service deletion, scaling, or advanced configuration through MCP","API key is account-scoped with no fine-grained permission control — grants access to all workspaces and services","No documented input/output schemas for service creation — exact parameters and configuration options unknown","Natural language interpretation depends entirely on the AI model's understanding; complex infrastructure requirements may not translate accurately"],"requires":["Active Render account with billing configured","Render API key generated from Account Settings","MCP-compatible AI application (Claude Desktop, Cursor, Codex, Claude Code, Jules, or Windsurf)","Network connectivity to Render API endpoints"],"input_types":["natural language prompts","service configuration parameters (framework, runtime, region)"],"output_types":["service object with ID, URL, and configuration","deployment status and service metadata"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-render__cap_1","uri":"capability://data.processing.analysis.database.query.execution.via.mcp","name":"database-query-execution-via-mcp","description":"Allows AI agents to execute queries against Render-hosted PostgreSQL databases through the MCP server, translating natural language database requests into SQL queries and returning structured result sets. The implementation acts as a query execution layer that maintains database connections and handles result serialization, enabling agents to analyze data, fetch records, and support debugging workflows without requiring direct database credentials in the agent's context.","intents":["I want Claude to query my production database to help debug data issues","I need my AI agent to fetch user records or analytics data to inform decisions","I want to enable natural language database exploration without exposing raw SQL or credentials"],"best_for":["Development teams using AI agents for rapid data exploration and debugging","Solo developers who want Claude to help analyze database state during development","Teams building AI-assisted analytics or reporting features"],"limitations":["Query language and syntax not documented — unclear if it supports complex joins, aggregations, or stored procedures","No documented query result pagination, filtering, or aggregation options","Database credentials are managed server-side; agents cannot directly connect, limiting advanced use cases","No mention of query timeout, result size limits, or performance constraints","Read-only capability implied but not explicitly stated — write operations may not be supported"],"requires":["Active Render PostgreSQL database instance","Render API key with database access permissions","MCP-compatible AI application configured with Render MCP server","Database must be accessible from Render's MCP server infrastructure"],"input_types":["natural language queries","database identifiers or service references"],"output_types":["structured query results (JSON or tabular format)","row counts and metadata"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-render__cap_2","uri":"capability://data.processing.analysis.service.metrics.and.logs.retrieval","name":"service-metrics-and-logs-retrieval","description":"Enables AI agents to retrieve and analyze service performance metrics and application logs from Render services through the MCP interface. The server queries Render's metrics and logging infrastructure, returning time-series data and log entries that agents can analyze to diagnose performance issues, identify errors, or understand service behavior. Metrics retention varies by Render plan (extended on Scale+ plans), and the MCP server abstracts the underlying metrics API.","intents":["I want Claude to analyze my service logs to identify the root cause of an error","I need my AI agent to check service metrics and alert me if performance degrades","I want to enable rapid debugging by having agents pull logs and metrics without manual dashboard access"],"best_for":["DevOps and SRE teams using AI for automated incident diagnosis","Solo developers debugging production issues with AI assistance","Teams building AI-powered monitoring and alerting systems"],"limitations":["Metrics retention policy not documented — unclear how far back historical data is available","No documented filtering, aggregation, or time-range specification for metrics queries","Log query syntax and filtering capabilities unknown","No mention of log sampling, pagination, or result size limits","Metrics available depend on Render plan tier; Scale+ plans have extended retention"],"requires":["Active Render service with metrics/logs available","Render API key with metrics and logs access","MCP-compatible AI application","Service must have been running long enough to generate metrics/logs"],"input_types":["service identifiers","time ranges (implied)","natural language analysis requests"],"output_types":["time-series metrics data (CPU, memory, request rate, etc.)","structured log entries with timestamps and severity","analysis summaries from AI interpretation"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-render__cap_3","uri":"capability://automation.workflow.service.environment.variable.configuration","name":"service-environment-variable-configuration","description":"Allows AI agents to read and modify environment variables for existing Render services through the MCP server. The implementation translates natural language configuration requests (e.g., 'set the database URL to...') into Render API calls that update service environment variables, with changes taking effect on the next service deployment. This is the only explicitly documented mutating operation beyond service creation.","intents":["I want Claude to update my service's database connection string without manual dashboard access","I need my AI agent to configure API keys and secrets for services dynamically","I want to enable rapid configuration changes during development and debugging"],"best_for":["Development teams using AI to manage service configuration during rapid iteration","Solo developers who want Claude to help configure services without dashboard access","Teams building AI-assisted deployment and configuration workflows"],"limitations":["Only supports modification of existing environment variables; no creation of new variables documented","Changes require service redeployment to take effect — no hot-reload capability","No validation or schema enforcement documented — agents could set invalid values","Broad API key scope means any AI agent with the key can modify any service's environment variables","No audit trail or change history documented for environment variable modifications"],"requires":["Active Render service with existing environment variables","Render API key with service configuration permissions","MCP-compatible AI application","Service must support redeployment for changes to take effect"],"input_types":["natural language configuration requests","environment variable names and values"],"output_types":["updated service configuration object","confirmation of changes applied"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-render__cap_4","uri":"capability://data.processing.analysis.service.discovery.and.inventory.listing","name":"service-discovery-and-inventory-listing","description":"Enables AI agents to list and discover all Render services in an account through the `list_services` tool, returning service metadata including IDs, names, types (web services, private services, background workers), and current status. This capability provides agents with visibility into the infrastructure landscape, enabling them to make informed decisions about which services to query, configure, or analyze.","intents":["I want Claude to show me all my services and their current status","I need my AI agent to find a specific service by name or type before performing operations","I want to enable agents to understand the full infrastructure context before making changes"],"best_for":["DevOps teams using AI for infrastructure visibility and decision-making","Solo developers who want Claude to help navigate their service inventory","Teams building AI-assisted infrastructure management tools"],"limitations":["No filtering or search capabilities documented — agents receive full service list regardless of relevance","Service metadata returned is not fully documented — unclear what fields are included beyond ID and name","No pagination or result limiting documented — could return very large lists for accounts with many services","No service relationship or dependency information — agents cannot understand service interconnections"],"requires":["Active Render account with at least one service","Render API key","MCP-compatible AI application"],"input_types":["no parameters required (lists all services)"],"output_types":["service list with metadata (IDs, names, types, status)","structured service objects"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-render__cap_5","uri":"capability://tool.use.integration.multi.application.mcp.server.integration","name":"multi-application-mcp-server-integration","description":"The Render MCP server is designed to integrate with multiple AI applications and IDEs through standardized MCP protocol configuration. Each application (Cursor, Codex, Claude Code, Claude Desktop, Jules, Windsurf) has its own configuration file format and location, and the MCP server adapts to each application's transport mechanism and authentication model. This enables a single Render API key to be used across multiple AI tools without requiring separate integrations.","intents":["I want to use Render MCP with my preferred AI IDE (Cursor, Claude Desktop, etc.)","I need to configure Render access across multiple development tools in my team","I want to switch between different AI applications while maintaining Render integration"],"best_for":["Teams using multiple AI IDEs and needing consistent Render integration across tools","Solo developers who want flexibility to use different AI applications","Organizations standardizing on MCP for infrastructure integration"],"limitations":["Each application requires separate configuration file management — no centralized configuration","Jules integration requires a separate Jules API key in addition to Render API key","Configuration formats vary by application (JSON for Cursor/Claude Desktop, TOML for Codex, CLI for Claude Code) — no unified configuration approach","Transport mechanism not documented — unclear if all applications use the same protocol or if adapters are required","No configuration validation or testing tools documented"],"requires":["Render API key","At least one MCP-compatible AI application installed (Cursor, Codex, Claude Code, Claude Desktop, Jules, or Windsurf)","Access to application configuration files or CLI tools","For Jules: separate Jules API key from dashboard.render.com/jules"],"input_types":["configuration file content (JSON/TOML)","API key values","application-specific configuration parameters"],"output_types":["configured MCP server connection","verified integration with AI application"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-render__cap_6","uri":"capability://safety.moderation.account.level.api.key.based.authentication","name":"account-level-api-key-based-authentication","description":"The Render MCP server uses account-scoped API keys for authentication, where a single key grants access to all workspaces and services within an account. The key is generated from the Render Account Settings page and passed to the MCP server via environment variables in each application's configuration. This approach provides account-wide access but lacks fine-grained permission scoping, creating a broad blast radius if the key is compromised.","intents":["I want to authenticate my AI agent with Render using a single API key","I need to grant my AI tools access to all my Render services without managing per-service credentials","I want to understand the security implications of sharing a Render API key with AI applications"],"best_for":["Solo developers and small teams where broad account access is acceptable","Development environments where security is less critical than convenience","Teams with strong API key management and rotation practices"],"limitations":["API key is account-scoped with no fine-grained permission control — grants access to all workspaces and services","No documented key rotation, expiration, or revocation mechanisms","Single key shared across all MCP configurations — compromising one application exposes all services","No audit logging of MCP-initiated operations documented (though Render platform has audit logs for Scale+ plans)","Broad scope creates significant security risk if key is exposed in logs, version control, or AI model context"],"requires":["Active Render account","Access to Account Settings page to generate API key","Secure storage mechanism for API key (environment variables, secrets manager, etc.)"],"input_types":["API key string"],"output_types":["authenticated MCP server connection","access to all account resources"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-render__cap_7","uri":"capability://automation.workflow.jules.pull.request.monitoring.and.auto.fix.integration","name":"jules-pull-request-monitoring-and-auto-fix-integration","description":"Jules, Render's AI code assistant, integrates with the Render MCP server to monitor pull requests and automatically push fixes to services. This capability requires a separate Jules API key (distinct from the Render API key) and must be explicitly enabled via a checkbox in the Jules integration settings. Jules can analyze code changes and automatically deploy fixes or configuration updates to Render services without manual intervention.","intents":["I want Jules to automatically fix and deploy issues detected in my pull requests","I need my AI assistant to monitor code changes and update Render service configuration automatically","I want to enable continuous AI-assisted deployment and remediation workflows"],"best_for":["Teams using Jules as their primary AI code assistant","Development workflows where automated fix deployment is acceptable","Teams with strong CI/CD practices and automated testing"],"limitations":["Requires separate Jules API key in addition to Render API key — adds credential management complexity","MCP features must be explicitly enabled via checkbox — not enabled by default","Auto-fix behavior not documented — unclear what types of issues Jules can detect and fix","No rollback or approval workflow documented — fixes are deployed automatically without human review","Jules integration is separate from other MCP applications — cannot use Jules features with Cursor, Claude Desktop, etc."],"requires":["Active Render account with services","Jules account and API key from dashboard.render.com/jules","Render API key","GitHub or GitLab repository connected to Jules","MCP features explicitly enabled in Jules integration settings"],"input_types":["pull request content and metadata","code changes and diffs"],"output_types":["automated fixes and code changes","deployed service updates","pull request comments with fix summaries"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Active Render account with billing configured","Render API key generated from Account Settings","MCP-compatible AI application (Claude Desktop, Cursor, Codex, Claude Code, Jules, or Windsurf)","Network connectivity to Render API endpoints","Active Render PostgreSQL database instance","Render API key with database access permissions","MCP-compatible AI application configured with Render MCP server","Database must be accessible from Render's MCP server infrastructure","Active Render service with metrics/logs available","Render API key with metrics and logs access"],"failure_modes":["Only supports environment variable modification as a destructive operation; no service deletion, scaling, or advanced configuration through MCP","API key is account-scoped with no fine-grained permission control — grants access to all workspaces and services","No documented input/output schemas for service creation — exact parameters and configuration options unknown","Natural language interpretation depends entirely on the AI model's understanding; complex infrastructure requirements may not translate accurately","Query language and syntax not documented — unclear if it supports complex joins, aggregations, or stored procedures","No documented query result pagination, filtering, or aggregation options","Database credentials are managed server-side; agents cannot directly connect, limiting advanced use cases","No mention of query timeout, result size limits, or performance constraints","Read-only capability implied but not explicitly stated — write operations may not be supported","Metrics retention policy not documented — unclear how far back historical data is available","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.048Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=render","compare_url":"https://unfragile.ai/compare?artifact=render"}},"signature":"kEDR3ct4Z+uttPce4/t6PtZvxQc19CY2JlvJNEt6cEH4LmvoZEmsnnK4x01UKb8fU3JAGEpanDWkaEGNsIxbCQ==","signedAt":"2026-06-21T12:50:15.443Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/render","artifact":"https://unfragile.ai/render","verify":"https://unfragile.ai/api/v1/verify?slug=render","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}