Skill Detail

Give coding agents runtime context for Encore services

Use Encore's AI integration and built-in MCP server so an agent can inspect service architecture, query local resources, call APIs, and analyze traces while iterating on TypeScript or Go backends.

Developer ToolsMCP
Developer Tools MCP Security Reviewed
⭐ 11.9k GitHub stars
INSTALL WITH ANY AGENT
npx skills add agentskillexchange/skills --skill give-coding-agents-runtime-context-for-encore-services Copy
Works best when you want a reusable capability, not another fragile one-off prompt.
At a glance
Tools required
Encore CLI; Encore TypeScript or Go project; agent runtime with MCP support
Install & setup
Install Encore with Homebrew, the upstream install script, or Windows PowerShell; add Encore's LLM instructions to the project; run the local Encore app; configure the built-in MCP server in the target agent runtime.
Author
Encore
Publisher
Open Source Project
Last updated
May 13, 2026
Quick brief

Approve this skill when an operator is maintaining an Encore application and wants an AI coding agent to work with live project context instead of guessing from source files alone. The workflow is to install Encore, add the upstream LLM instructions to the project, run the local Encore environment, connect the built-in MCP server to the chosen agent runtime, and let the agent inspect architecture, query databases, call APIs, and review traces while proposing or debugging backend changes. Use this instead of the product’s normal dashboard when the goal is an agent-assisted development or debugging loop with explicit human review. The scope boundary is Encore-backed service inspection and local development context; it is not a generic cloud platform listing or a replacement for deploying and operating Encore Cloud.

How it works

What this skill actually does

Inputs and prerequisites: Encore CLI; Encore TypeScript or Go project; agent runtime with MCP support.

Setup notes: Install Encore with Homebrew, the upstream install script, or Windows PowerShell; add Encore’s LLM instructions to the project; run the local Encore app; configure the built-in MCP server in the target agent runtime.

Source and verification boundary: use https://encore.dev/docs/ts/ai-integration as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.

Framework fit: publish this as a MCP workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.